eBay:iPads:: sold classification:: template
bdanalytics
Date: (Thu) Jul 16, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: Low.cor.X.glm: Leaderboard: 0.83458 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "template_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "sold"
# for classification, the response variable has to be a factor
glb_rsp_var <- "sold.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
# return(log(raw))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))## [1] Y Y N N <NA>
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
# return(exp(var))
as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))## [1] 1 1 0 0 NAif ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
glb_id_var <- c("UniqueID")
glb_category_vars <- NULL # or c("<var1>", "<var2>")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
# glb_derive_lst[["<feat.sfx>"]] <- list(
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "PropR.fctr"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- c("description")
#Sys.setlocale("LC_ALL", "C") # For english
glb_txt_munge_filenames_pfx <- "mytxt_"
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
# glb_append_stop_words[["<txt_var1>"]] <- c(NULL
# # ,"<word1>" # <reason1>
# )
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_sprs_thresholds <- c(0.950) # Generates ??? terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- NULL # or c("<var_name>")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # or TRUE
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 7.793 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \x89\xdb\xcfUsed Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 7.793 8.13 0.337
## 2 inspect.data 2 0 8.131 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## Loading required package: reshape2
## sold.0 sold.1 sold.NA
## Test NA NA 798
## Train 1001 860 NA
## sold.0 sold.1 sold.NA
## Test NA NA 1
## Train 0.5378829 0.4621171 NA
## [1] "numeric data missing in glb_allobs_df: "
## sold
## 798
## [1] "numeric data w/ 0s in glb_allobs_df: "
## biddable sold
## 1446 1001
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline
## 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## sold sold.fctr .n
## 1 0 N 1001
## 2 1 Y 860
## 3 NA <NA> 798## Warning: Removed 1 rows containing missing values (position_stack).
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 798
## Train 1001 860 NA
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 1
## Train 0.5378829 0.4621171 NA# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"
## [1] "feat: startprice"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 8.131 14.007 5.876
## 3 scrub.data 2 1 14.008 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in glb_allobs_df: "
## biddable sold
## 1446 1001
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline
## 0dsp_catgs <- function() {
print("NewsDesk:")
print(table(glb_allobs_df$NewsDesk))
print("SectionName:")
print(table(glb_allobs_df$SectionName))
print("SubsectionName:")
print(table(glb_allobs_df$SubsectionName))
}
# sel_obs <- function(Popular=NULL,
# NewsDesk=NULL, SectionName=NULL, SubsectionName=NULL,
# Headline.contains=NULL, Snippet.contains=NULL, Abstract.contains=NULL,
# Headline.pfx=NULL, NewsDesk.nb=NULL, .clusterid=NULL, myCategory=NULL,
# perl=FALSE) {
sel_obs <- function(vars_lst) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
if (!is.null(Popular)) {
if (is.na(Popular))
tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
}
if (!is.null(NewsDesk))
tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
if (!is.null(SectionName))
tmp_df <- tmp_df[tmp_df$SectionName == SectionName, ]
if (!is.null(SubsectionName))
tmp_df <- tmp_df[tmp_df$SubsectionName == SubsectionName, ]
if (!is.null(Headline.contains))
tmp_df <-
tmp_df[grep(Headline.contains, tmp_df$Headline, perl=perl), ]
if (!is.null(Snippet.contains))
tmp_df <-
tmp_df[grep(Snippet.contains, tmp_df$Snippet, perl=perl), ]
if (!is.null(Abstract.contains))
tmp_df <-
tmp_df[grep(Abstract.contains, tmp_df$Abstract, perl=perl), ]
if (!is.null(Headline.pfx)) {
if (length(grep("Headline.pfx", names(tmp_df), fixed=TRUE, value=TRUE))
> 0) tmp_df <-
tmp_df[tmp_df$Headline.pfx == Headline.pfx, ] else
warning("glb_allobs_df does not contain Headline.pfx; ignoring that filter")
}
if (!is.null(NewsDesk.nb)) {
if (any(grepl("NewsDesk.nb", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$NewsDesk.nb == NewsDesk.nb, ] else
warning("glb_allobs_df does not contain NewsDesk.nb; ignoring that filter")
}
if (!is.null(.clusterid)) {
if (any(grepl(".clusterid", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$clusterid == clusterid, ] else
warning("glb_allobs_df does not contain clusterid; ignoring that filter") }
if (!is.null(myCategory)) {
if (!(myCategory %in% names(glb_allobs_df)))
tmp_df <-
tmp_df[tmp_df$myCategory == myCategory, ] else
warning("glb_allobs_df does not contain myCategory; ignoring that filter")
}
return(glb_allobs_df$UniqueID %in% tmp_df$UniqueID)
}
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
union(c("UniqueID", "Popular", "myCategory", "Headline"), cols), FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_tbl <- function(...) {
tmp_entity_df <- glb_allobs_df[sel_obs(...), ]
tmp_tbl <- table(tmp_entity_df$NewsDesk,
tmp_entity_df$SectionName,
tmp_entity_df$SubsectionName,
tmp_entity_df$Popular, useNA="ifany")
#print(names(tmp_tbl))
#print(dimnames(tmp_tbl))
print(tmp_tbl)
}
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Create myCategory <- NewsDesk#SectionName#SubsectionName
# Fix some data before merging categories
# glb_allobs_df[sel_obs(Headline.contains="Your Turn:", NewsDesk=""),
# "NewsDesk"] <- "Styles"
# glb_allobs_df[sel_obs(Headline.contains="School", NewsDesk="", SectionName="U.S.",
# SubsectionName=""),
# "SubsectionName"] <- "Education"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SectionName"] <- "Business Day"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SubsectionName"] <- "Small Business"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SectionName"] <- "Opinion"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SubsectionName"] <- "Room For Debate"
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName="", Popular=NA),
# "SubsectionName"] <- "Small Business"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(7973),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName=""),
# "SectionName"] <- "Technology"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5076, 5736, 5924, 5911, 6532),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(SectionName="Health"),
# "NewsDesk"] <- "Science"
# glb_allobs_df[sel_obs(SectionName="Travel"),
# "NewsDesk"] <- "Travel"
#
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SectionName"] <- ""
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SubsectionName"] <- ""
# glb_allobs_df[sel_obs(NewsDesk="Styles", SectionName="", SubsectionName="", Popular=1),
# "SectionName"] <- "U.S."
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5486),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df$myCategory <- paste(glb_allobs_df$NewsDesk,
# glb_allobs_df$SectionName,
# glb_allobs_df$SubsectionName,
# sep="#")
# dsp_obs( Headline.contains="Music:"
# #,NewsDesk=""
# #,SectionName=""
# #,SubsectionName="Fashion & Style"
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# dsp_obs( Headline.contains="."
# ,NewsDesk=""
# ,SectionName="Opinion"
# ,SubsectionName=""
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "#Crosswords/Games#" = "Business#Crosswords/Games#",
# "Business##" = "Business#Technology#",
# "#Open#" = "Business#Technology#",
# "#Technology#" = "Business#Technology#",
#
# "#Arts#" = "Culture#Arts#",
# "Culture##" = "Culture#Arts#",
#
# "#World#Asia Pacific" = "Foreign#World#Asia Pacific",
# "Foreign##" = "Foreign#World#",
#
# "#N.Y. / Region#" = "Metro#N.Y. / Region#",
#
# "#Opinion#" = "OpEd#Opinion#",
# "OpEd##" = "OpEd#Opinion#",
#
# "#Health#" = "Science#Health#",
# "Science##" = "Science#Health#",
#
# "Styles##" = "Styles##Fashion",
# "Styles#Health#" = "Science#Health#",
# "Styles#Style#Fashion & Style" = "Styles##Fashion",
#
# "#Travel#" = "Travel#Travel#",
#
# "Magazine#Magazine#" = "myOther",
# "National##" = "myOther",
# "National#U.S.#Politics" = "myOther",
# "Sports##" = "myOther",
# "Sports#Sports#" = "myOther",
# "#U.S.#" = "myOther",
#
#
# # "Business##Small Business" = "Business#Business Day#Small Business",
# #
# # "#Opinion#" = "#Opinion#Room For Debate",
# "##" = "##"
# # "Business##" = "Business#Business Day#Dealbook",
# # "Foreign#World#" = "Foreign##",
# # "#Open#" = "Other",
# # "#Opinion#The Public Editor" = "OpEd#Opinion#",
# # "Styles#Health#" = "Styles##",
# # "Styles#Style#Fashion & Style" = "Styles##",
# # "#U.S.#" = "#U.S.#Education",
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
# Copy Headline into Snipper & Abstract if they are empty
# print(glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, c("Headline", "Snippet")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Snippet,
# c("UniqueID", "Headline", "Snippet")])
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Snippet"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Headline"]
#
# print(glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, c("Headline", "Abstract")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Abstract,
# c("UniqueID", "Headline", "Abstract")])
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Abstract"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Headline"]
# WordCount_0_df <- subset(glb_allobs_df, WordCount == 0)
# table(WordCount_0_df$Popular, WordCount_0_df$WordCount, useNA="ifany")
# myprint_df(WordCount_0_df[,
# c("UniqueID", "Popular", "WordCount", "Headline")])Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 14.008 14.994 0.986
## 4 transform.data 2 2 14.994 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 14.994 15.057 0.063
## 5 extract.features 3 0 15.058 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 15.064 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 15.064 15.079
## 2 extract.features_factorize.str.vars 2 0 15.079 NA
## elapsed
## 1 0.015
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src
## "storage" "productline" ".src"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5## Warning: Creating factors of string variable: productline: # of unique
## values: 12if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[163, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE,
preserve_intra_word_dashes=TRUE, lazy=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE) #nuppr
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
# Not to be run in production
inspect_terms <- function() {
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full +term, full_Tf_df)
print(myplot_histogram(full_Tf_df, "Tf.full"))
myprint_df(full_Tf_df)
#txt_corpus[[which(grepl("zun", txt_vctr, ignore.case=TRUE))]]
digit_terms_df <- subset(full_Tf_df, grepl("[[:digit:]]", term))
myprint_df(digit_terms_df)
return(full_Tf_df)
}
#print("RemovePunct:"); remove_punct_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")), lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, stemDocument, lazy=TRUE) #Features for lost information: Difference/ratio in density of full_TfIdf_DTM ???
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_Tf_df <- inspect_terms()
#stemmed_stopped_Tf_df <- merge(stemmed_words_Tf_df, stopped_words_Tf_df, by="term", all=TRUE, suffixes=c(".stem", ".stop"))
#myprint_df(stemmed_stopped_Tf_df)
#print(subset(stemmed_stopped_Tf_df, grepl("compan", term)))
#glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
#grep("scene", names(full_TfIdf_vctr), value=TRUE)
#which.max(full_TfIdf_mtrx[, "yearlong"])
full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
names(full_TfIdf_df) <- "TfIdf.full"
full_TfIdf_df$term <- rownames(full_TfIdf_df)
full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
names(sprs_TfIdf_df) <- "TfIdf.sprs"
sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_X_df <- cbind(txt_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today", xcol_name=glb_rsp_var))
# log_X_df <- log(1 + txt_X_df)
# colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
# plt_X_df <- cbind(log_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today.log", xcol_name=glb_rsp_var))
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#sav_allobs_df <- glb_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
#txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 15.079 15.589
## 3 extract.features_process.text 3 0 15.589 NA
## elapsed
## 2 0.51
## 3 NA
## [1] "Building glb_txt_lst..."
## [1] "running gsub for 10 (of 171): #\\bA\\.D\\.P\\.#..."
## [1] "running gsub for 20 (of 171): #\\bA\\.P\\.#..."
## [1] "running gsub for 30 (of 171): #\\bC\\.I\\.C\\.C\\.#..."
## [1] "running gsub for 40 (of 171): #\\bE\\.J\\. Manuel#..."
## [1] "running gsub for 50 (of 171): #\\bF\\.T\\.C\\.#..."
## [1] "running gsub for 60 (of 171): #\\bI\\.M\\.F\\.#..."
## [1] "running gsub for 70 (of 171): #\\bJ\\.F\\.K\\.#..."
## [1] "running gsub for 80 (of 171): #\\bM\\.&A\\.#..."
## [1] "running gsub for 90 (of 171): #\\bN\\.R\\.A\\.#..."
## [1] "running gsub for 100 (of 171): #\\bP\\.R\\.#..."
## [1] "running gsub for 110 (of 171): #\\bS\\.E\\.O\\.#..."
## [1] "running gsub for 120 (of 171): #\\bU\\.S\\.(A\\.)*#..."
## [1] "running gsub for 130 (of 171): #\\bW\\.M\\.D\\.#..."
## [1] "running gsub for 140 (of 171): #\\bLos( |-)((Angeles|Angeless)\\b)+#..."
## [1] "running gsub for 150 (of 171): #(\\w)-(Shanghai|USA)\\b#..."
## [1] "running gsub for 160 (of 171): #(one|two|three|four|five|six|seven|eight|nine)-#..."
## [1] "running gsub for 170 (of 171): #\\b[Ss]eventy-[Oo]ne\\b#..."
## [1] "Remaining OK in description:"
## pattern .n
## 1 OK 7
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN DEVICE: Problem with Apple ID"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN SCREEN"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in description:"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## pattern .n
## 1 ONLY. 7
## 2 CONDITION. 6
## 3 GB. 4
## 4 BOX. 2
## 5 CONDITION. 2
## 6 CORNER. 2
## 7 ESN. 2
## 8 GOOD. 2
## 9 ICLOUD. 2
## 10 IMEI. 2
## 11 IPADS. 2
## 12 LOCKED. 2
## 13 LOCKS. 2
## 14 ON. 2
## 15 SCRATCHES. 2
## 16 TEARS. 2
## 17 USE. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in description: "
## pattern .n
## 2 New Open 3
## 4 New Condition 2
## 7 New Digitizer 1
## 8 New Opened 1
## 9 New Scratch 1
## 10 New Screen 1
## 14 New-Other 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in description: "
## pattern .n
## 1 C Stock 3
## 2 W blue 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in description: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed
## 3 0 15.589 17.962 2.373
## 4 1 17.962 NA NA
## [1] "Remaining compound terms in description: "
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor bgn end elapsed
## 4 1 17.962 17.967 0.005
## 5 0 17.967 NA NA
## [1] "Building glb_corpus_lst..."
## label step_major step_minor bgn end
## 5 extract.features_build.corpus 4 0 17.967 18.588
## 6 extract.features_extract.DTM 5 0 18.589 NA
## elapsed
## 5 0.621
## 6 NA
## [1] "Extracting TfIDf terms for description..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 18.589 21.95 3.362
## 7 extract.features_report.DTM 6 0 21.951 NA NA
## [1] "Reporting TfIDf terms for description..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2659, terms: 1123)>>
## Non-/sparse entries: 9175/2976882
## Sparsity : 100%
## Maximal term length: 27
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2659, terms: 10)>>
## Non-/sparse entries: 1989/24601
## Sparsity : 93%
## Maximal term length: 9
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).
## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 21.951 24.492 2.541
## 8 extract.features_bind.DTM 7 0 24.493 NA NA
## [1] "Binding DTM for description..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 24.493 24.502 0.009
## 9 extract.features_bind.DXM 8 0 24.503 NA NA
## [1] "Binding DXM for description..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 24.503 31.211 6.708
## 10 extract.features_end 9 0 31.211 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 6 extract.features_extract.DTM 5
## 7 extract.features_report.DTM 6
## 3 extract.features_process.text 3
## 5 extract.features_build.corpus 4
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## 8 extract.features_bind.DTM 7
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 24.503 31.211 6.708 6.708
## 6 0 18.589 21.950 3.362 3.361
## 7 0 21.951 24.492 2.541 2.541
## 3 0 15.589 17.962 2.373 2.373
## 5 0 17.967 18.588 0.621 0.621
## 2 0 15.079 15.589 0.510 0.510
## 1 0 15.064 15.079 0.015 0.015
## 8 0 24.493 24.502 0.009 0.009
## 4 1 17.962 17.967 0.005 0.005
## [1] "Total Elapsed Time: 31.211 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 15.058 32.681 17.624
## 6 cluster.data 4 0 32.682 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 32.682 33.602 0.92
## 7 manage.missing.data 4 1 33.602 NA NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold cellular.fctr
## 1446 1001 1595
## D.T.condit D.T.condition D.T.good
## 2455 2385 2463
## D.T.ipad D.T.new D.T.scratch
## 2432 2520 2431
## D.T.screen D.T.this D.T.use
## 2453 2503 2474
## D.T.work D.T.box D.T.excellent
## 2485 2553 2602
## D.T.great D.T.like D.T.used
## 2539 2591 2540
## D.T.veri D.nwrds.log D.nwrds.unq.log
## 2567 1521 1529
## D.sum.TfIdf D.ratio.sum.TfIdf.nwrds D.nchrs.log
## 1529 1529 1521
## D.nuppr.log D.ndgts.log D.npnct01.log
## 1583 2428 2581
## D.npnct02.log D.npnct03.log D.npnct04.log
## 2659 2616 2659
## D.npnct05.log D.npnct06.log D.npnct07.log
## 2594 2556 2659
## D.npnct08.log D.npnct09.log D.npnct10.log
## 2583 2643 2650
## D.npnct11.log D.npnct12.log D.npnct13.log
## 2303 2539 1934
## D.npnct14.log D.npnct15.log D.npnct16.log
## 2584 2638 2548
## D.npnct17.log D.npnct18.log D.npnct19.log
## 2659 2658 2659
## D.npnct20.log D.npnct21.log D.npnct22.log
## 2659 2659 2659
## D.npnct23.log D.npnct24.log D.npnct25.log
## 2659 1521 2659
## D.npnct26.log D.npnct27.log D.npnct28.log
## 2659 2659 2651
## D.npnct29.log D.npnct30.log D.nstopwrds.log
## 2659 2659 1761
## D.P.http
## 2659
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline
## 0# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold cellular.fctr
## 1446 1001 1595
## D.T.condit D.T.condition D.T.good
## 2455 2385 2463
## D.T.ipad D.T.new D.T.scratch
## 2432 2520 2431
## D.T.screen D.T.this D.T.use
## 2453 2503 2474
## D.T.work D.T.box D.T.excellent
## 2485 2553 2602
## D.T.great D.T.like D.T.used
## 2539 2591 2540
## D.T.veri D.nwrds.log D.nwrds.unq.log
## 2567 1521 1529
## D.sum.TfIdf D.ratio.sum.TfIdf.nwrds D.nchrs.log
## 1529 1529 1521
## D.nuppr.log D.ndgts.log D.npnct01.log
## 1583 2428 2581
## D.npnct02.log D.npnct03.log D.npnct04.log
## 2659 2616 2659
## D.npnct05.log D.npnct06.log D.npnct07.log
## 2594 2556 2659
## D.npnct08.log D.npnct09.log D.npnct10.log
## 2583 2643 2650
## D.npnct11.log D.npnct12.log D.npnct13.log
## 2303 2539 1934
## D.npnct14.log D.npnct15.log D.npnct16.log
## 2584 2638 2548
## D.npnct17.log D.npnct18.log D.npnct19.log
## 2659 2658 2659
## D.npnct20.log D.npnct21.log D.npnct22.log
## 2659 2659 2659
## D.npnct23.log D.npnct24.log D.npnct25.log
## 2659 1521 2659
## D.npnct26.log D.npnct27.log D.npnct28.log
## 2659 2659 2651
## D.npnct29.log D.npnct30.log D.nstopwrds.log
## 2659 2659 1761
## D.P.http
## 2659
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline
## 0Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}
# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 33.602 33.745 0.143
## 8 select.features 5 0 33.746 NA NAStep 5.0: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## sold sold 1.0000000000
## biddable biddable 0.5485860292
## startprice startprice -0.4556701005
## UniqueID UniqueID -0.1904241613
## condition.fctr condition.fctr -0.1531007887
## D.npnct05.log D.npnct05.log -0.1178427734
## productline.fctr productline.fctr -0.0895301600
## D.npnct14.log D.npnct14.log -0.0784119354
## cellular.fctr cellular.fctr -0.0770423554
## D.nuppr.log D.nuppr.log -0.0702062921
## carrier.fctr carrier.fctr -0.0677807650
## D.ndgts.log D.ndgts.log -0.0633901377
## D.nwrds.unq.log D.nwrds.unq.log -0.0619524203
## D.npnct09.log D.npnct09.log -0.0617301891
## D.ratio.nstopwrds.nwrds D.ratio.nstopwrds.nwrds 0.0596974338
## D.nwrds.log D.nwrds.log -0.0587159086
## D.nchrs.log D.nchrs.log -0.0565815056
## D.npnct12.log D.npnct12.log -0.0549329137
## D.T.like D.T.like -0.0533938586
## D.npnct28.log D.npnct28.log -0.0523776347
## D.T.box D.T.box -0.0515596986
## D.npnct06.log D.npnct06.log -0.0497583609
## D.T.this D.T.this -0.0477867670
## D.npnct24.log D.npnct24.log -0.0459503420
## D.npnct16.log D.npnct16.log -0.0447153098
## storage.fctr storage.fctr -0.0444449992
## color.fctr color.fctr -0.0426291775
## D.T.condit D.T.condit -0.0415362400
## D.npnct15.log D.npnct15.log 0.0400921803
## D.npnct08.log D.npnct08.log -0.0394718187
## D.npnct13.log D.npnct13.log -0.0349433657
## D.T.screen D.T.screen 0.0312904991
## D.T.new D.T.new -0.0302966020
## D.sum.TfIdf D.sum.TfIdf -0.0300344031
## D.nstopwrds.log D.nstopwrds.log -0.0278756193
## D.npnct03.log D.npnct03.log 0.0258721719
## D.T.used D.T.used 0.0251046087
## D.npnct10.log D.npnct10.log -0.0240301079
## D.T.ipad D.T.ipad -0.0234288421
## .rnorm .rnorm -0.0228561937
## D.npnct18.log D.npnct18.log -0.0214919447
## D.T.excellent D.T.excellent 0.0203632794
## D.npnct11.log D.npnct11.log -0.0188179512
## D.T.great D.T.great 0.0171206415
## D.ratio.sum.TfIdf.nwrds D.ratio.sum.TfIdf.nwrds 0.0119528209
## D.T.condition D.T.condition -0.0118174892
## D.T.use D.T.use -0.0097129508
## D.T.scratch D.T.scratch -0.0091382072
## D.T.good D.T.good 0.0063426895
## D.npnct01.log D.npnct01.log 0.0042941114
## D.T.work D.T.work -0.0035586740
## D.T.veri D.T.veri -0.0001659508
## D.npnct02.log D.npnct02.log NA
## D.npnct04.log D.npnct04.log NA
## D.npnct07.log D.npnct07.log NA
## D.npnct17.log D.npnct17.log NA
## D.npnct19.log D.npnct19.log NA
## D.npnct20.log D.npnct20.log NA
## D.npnct21.log D.npnct21.log NA
## D.npnct22.log D.npnct22.log NA
## D.npnct23.log D.npnct23.log NA
## D.npnct25.log D.npnct25.log NA
## D.npnct26.log D.npnct26.log NA
## D.npnct27.log D.npnct27.log NA
## D.npnct29.log D.npnct29.log NA
## D.npnct30.log D.npnct30.log NA
## D.P.http D.P.http NA
## exclude.as.feat cor.y.abs
## sold 1 1.0000000000
## biddable 0 0.5485860292
## startprice 0 0.4556701005
## UniqueID 1 0.1904241613
## condition.fctr 0 0.1531007887
## D.npnct05.log 0 0.1178427734
## productline.fctr 0 0.0895301600
## D.npnct14.log 0 0.0784119354
## cellular.fctr 0 0.0770423554
## D.nuppr.log 0 0.0702062921
## carrier.fctr 0 0.0677807650
## D.ndgts.log 0 0.0633901377
## D.nwrds.unq.log 0 0.0619524203
## D.npnct09.log 0 0.0617301891
## D.ratio.nstopwrds.nwrds 0 0.0596974338
## D.nwrds.log 0 0.0587159086
## D.nchrs.log 0 0.0565815056
## D.npnct12.log 0 0.0549329137
## D.T.like 0 0.0533938586
## D.npnct28.log 0 0.0523776347
## D.T.box 0 0.0515596986
## D.npnct06.log 0 0.0497583609
## D.T.this 0 0.0477867670
## D.npnct24.log 0 0.0459503420
## D.npnct16.log 0 0.0447153098
## storage.fctr 0 0.0444449992
## color.fctr 0 0.0426291775
## D.T.condit 0 0.0415362400
## D.npnct15.log 0 0.0400921803
## D.npnct08.log 0 0.0394718187
## D.npnct13.log 0 0.0349433657
## D.T.screen 0 0.0312904991
## D.T.new 0 0.0302966020
## D.sum.TfIdf 0 0.0300344031
## D.nstopwrds.log 0 0.0278756193
## D.npnct03.log 0 0.0258721719
## D.T.used 0 0.0251046087
## D.npnct10.log 0 0.0240301079
## D.T.ipad 0 0.0234288421
## .rnorm 0 0.0228561937
## D.npnct18.log 0 0.0214919447
## D.T.excellent 0 0.0203632794
## D.npnct11.log 0 0.0188179512
## D.T.great 0 0.0171206415
## D.ratio.sum.TfIdf.nwrds 0 0.0119528209
## D.T.condition 0 0.0118174892
## D.T.use 0 0.0097129508
## D.T.scratch 0 0.0091382072
## D.T.good 0 0.0063426895
## D.npnct01.log 0 0.0042941114
## D.T.work 0 0.0035586740
## D.T.veri 0 0.0001659508
## D.npnct02.log 0 NA
## D.npnct04.log 0 NA
## D.npnct07.log 0 NA
## D.npnct17.log 0 NA
## D.npnct19.log 0 NA
## D.npnct20.log 0 NA
## D.npnct21.log 0 NA
## D.npnct22.log 0 NA
## D.npnct23.log 0 NA
## D.npnct25.log 0 NA
## D.npnct26.log 0 NA
## D.npnct27.log 0 NA
## D.npnct29.log 0 NA
## D.npnct30.log 0 NA
## D.P.http 0 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(D.nchrs.log, D.nwrds.log)=0.9931"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0566"
## [1] "cor(sold.fctr, D.nwrds.log)=-0.0587"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nchrs.log as highly correlated with
## D.nwrds.log## [1] "cor(D.nwrds.log, D.nwrds.unq.log)=0.9892"
## [1] "cor(sold.fctr, D.nwrds.log)=-0.0587"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.npnct24.log, D.ratio.nstopwrds.nwrds)=-0.9691"
## [1] "cor(sold.fctr, D.npnct24.log)=-0.0460"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0597"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct24.log as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.npnct06.log, D.npnct16.log)=0.9556"
## [1] "cor(sold.fctr, D.npnct06.log)=-0.0498"
## [1] "cor(sold.fctr, D.npnct16.log)=-0.0447"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct16.log as highly correlated with
## D.npnct06.log## [1] "cor(D.nwrds.unq.log, D.sum.TfIdf)=0.9437"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"
## [1] "cor(sold.fctr, D.sum.TfIdf)=-0.0300"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.sum.TfIdf as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.ratio.nstopwrds.nwrds)=-0.9340"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0597"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.nstopwrds.nwrds as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nstopwrds.log, D.nwrds.unq.log)=0.8370"
## [1] "cor(sold.fctr, D.nstopwrds.log)=-0.0279"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nstopwrds.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(carrier.fctr, cellular.fctr)=0.8345"
## [1] "cor(sold.fctr, carrier.fctr)=-0.0678"
## [1] "cor(sold.fctr, cellular.fctr)=-0.0770"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr## [1] "cor(D.nuppr.log, D.nwrds.unq.log)=0.8199"
## [1] "cor(sold.fctr, D.nuppr.log)=-0.0702"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.unq.log as highly correlated with
## D.nuppr.log## [1] "cor(D.T.like, D.T.new)=0.7047"
## [1] "cor(sold.fctr, D.T.like)=-0.0534"
## [1] "cor(sold.fctr, D.T.new)=-0.0303"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.T.new as highly correlated with D.T.like## id cor.y exclude.as.feat cor.y.abs
## 64 sold 1.0000000000 1 1.0000000000
## 2 biddable 0.5485860292 0 0.5485860292
## 44 D.ratio.nstopwrds.nwrds 0.0596974338 0 0.0596974338
## 23 D.npnct15.log 0.0400921803 0 0.0400921803
## 57 D.T.screen 0.0312904991 0 0.0312904991
## 11 D.npnct03.log 0.0258721719 0 0.0258721719
## 60 D.T.used 0.0251046087 0 0.0251046087
## 50 D.T.excellent 0.0203632794 0 0.0203632794
## 52 D.T.great 0.0171206415 0 0.0171206415
## 45 D.ratio.sum.TfIdf.nwrds 0.0119528209 0 0.0119528209
## 51 D.T.good 0.0063426895 0 0.0063426895
## 9 D.npnct01.log 0.0042941114 0 0.0042941114
## 61 D.T.veri -0.0001659508 0 0.0001659508
## 62 D.T.work -0.0035586740 0 0.0035586740
## 56 D.T.scratch -0.0091382072 0 0.0091382072
## 59 D.T.use -0.0097129508 0 0.0097129508
## 49 D.T.condition -0.0118174892 0 0.0118174892
## 19 D.npnct11.log -0.0188179512 0 0.0188179512
## 26 D.npnct18.log -0.0214919447 0 0.0214919447
## 1 .rnorm -0.0228561937 0 0.0228561937
## 53 D.T.ipad -0.0234288421 0 0.0234288421
## 18 D.npnct10.log -0.0240301079 0 0.0240301079
## 39 D.nstopwrds.log -0.0278756193 0 0.0278756193
## 46 D.sum.TfIdf -0.0300344031 0 0.0300344031
## 55 D.T.new -0.0302966020 0 0.0302966020
## 21 D.npnct13.log -0.0349433657 0 0.0349433657
## 16 D.npnct08.log -0.0394718187 0 0.0394718187
## 48 D.T.condit -0.0415362400 0 0.0415362400
## 5 color.fctr -0.0426291775 0 0.0426291775
## 66 storage.fctr -0.0444449992 0 0.0444449992
## 24 D.npnct16.log -0.0447153098 0 0.0447153098
## 32 D.npnct24.log -0.0459503420 0 0.0459503420
## 58 D.T.this -0.0477867670 0 0.0477867670
## 14 D.npnct06.log -0.0497583609 0 0.0497583609
## 47 D.T.box -0.0515596986 0 0.0515596986
## 36 D.npnct28.log -0.0523776347 0 0.0523776347
## 54 D.T.like -0.0533938586 0 0.0533938586
## 20 D.npnct12.log -0.0549329137 0 0.0549329137
## 7 D.nchrs.log -0.0565815056 0 0.0565815056
## 41 D.nwrds.log -0.0587159086 0 0.0587159086
## 17 D.npnct09.log -0.0617301891 0 0.0617301891
## 42 D.nwrds.unq.log -0.0619524203 0 0.0619524203
## 8 D.ndgts.log -0.0633901377 0 0.0633901377
## 3 carrier.fctr -0.0677807650 0 0.0677807650
## 40 D.nuppr.log -0.0702062921 0 0.0702062921
## 4 cellular.fctr -0.0770423554 0 0.0770423554
## 22 D.npnct14.log -0.0784119354 0 0.0784119354
## 63 productline.fctr -0.0895301600 0 0.0895301600
## 13 D.npnct05.log -0.1178427734 0 0.1178427734
## 6 condition.fctr -0.1531007887 0 0.1531007887
## 67 UniqueID -0.1904241613 1 0.1904241613
## 65 startprice -0.4556701005 0 0.4556701005
## 10 D.npnct02.log NA 0 NA
## 12 D.npnct04.log NA 0 NA
## 15 D.npnct07.log NA 0 NA
## 25 D.npnct17.log NA 0 NA
## 27 D.npnct19.log NA 0 NA
## 28 D.npnct20.log NA 0 NA
## 29 D.npnct21.log NA 0 NA
## 30 D.npnct22.log NA 0 NA
## 31 D.npnct23.log NA 0 NA
## 33 D.npnct25.log NA 0 NA
## 34 D.npnct26.log NA 0 NA
## 35 D.npnct27.log NA 0 NA
## 37 D.npnct29.log NA 0 NA
## 38 D.npnct30.log NA 0 NA
## 43 D.P.http NA 0 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## 64 <NA> 1.163953 0.10746910 FALSE FALSE
## 2 <NA> 1.223417 0.10746910 FALSE FALSE
## 44 D.nwrds.unq.log 14.280000 4.83610962 FALSE FALSE
## 23 <NA> 153.500000 0.16120365 FALSE TRUE
## 57 <NA> 45.105263 0.80601827 FALSE TRUE
## 11 <NA> 83.318182 0.16120365 FALSE TRUE
## 60 <NA> 161.818182 0.85975282 FALSE TRUE
## 50 <NA> 227.875000 0.80601827 FALSE TRUE
## 52 <NA> 127.214286 0.75228372 FALSE TRUE
## 45 <NA> 63.294118 35.14239656 FALSE FALSE
## 51 <NA> 39.181818 1.02095648 FALSE TRUE
## 9 <NA> 53.029412 0.32240731 FALSE TRUE
## 61 <NA> 163.090909 0.85975282 FALSE TRUE
## 62 <NA> 82.761905 0.91348737 FALSE TRUE
## 56 <NA> 35.416667 0.80601827 FALSE TRUE
## 59 <NA> 57.533333 0.96722192 FALSE TRUE
## 49 <NA> 52.375000 0.80601827 FALSE TRUE
## 19 <NA> 9.385965 0.37614186 FALSE FALSE
## 26 <NA> 1860.000000 0.10746910 FALSE TRUE
## 1 <NA> 1.000000 100.00000000 FALSE FALSE
## 53 <NA> 51.484848 0.91348737 FALSE TRUE
## 18 <NA> 309.000000 0.16120365 FALSE TRUE
## 39 D.nwrds.unq.log 14.726190 0.75228372 FALSE FALSE
## 46 D.nwrds.unq.log 63.294118 34.87372380 FALSE FALSE
## 55 D.T.like 147.166667 0.91348737 FALSE TRUE
## 21 <NA> 5.210728 0.48361096 FALSE FALSE
## 16 <NA> 69.653846 0.21493821 FALSE TRUE
## 48 <NA> 52.000000 0.85975282 FALSE TRUE
## 5 <NA> 1.576837 0.26867276 FALSE FALSE
## 66 <NA> 2.747059 0.26867276 FALSE FALSE
## 24 D.npnct06.log 31.280702 0.16120365 FALSE TRUE
## 32 D.ratio.nstopwrds.nwrds 1.355696 0.10746910 FALSE FALSE
## 58 <NA> 40.744186 0.69854917 FALSE TRUE
## 14 <NA> 33.773585 0.16120365 FALSE TRUE
## 47 <NA> 111.875000 0.85975282 FALSE TRUE
## 36 <NA> 463.750000 0.16120365 FALSE TRUE
## 54 <NA> 259.142857 0.85975282 FALSE TRUE
## 20 <NA> 27.261538 0.21493821 FALSE TRUE
## 7 D.nwrds.log 14.875000 5.64212789 FALSE FALSE
## 41 D.nwrds.unq.log 12.310345 1.39709833 FALSE FALSE
## 17 <NA> 308.666667 0.21493821 FALSE TRUE
## 42 D.nuppr.log 7.685714 0.91348737 FALSE FALSE
## 8 <NA> 27.063492 0.69854917 FALSE TRUE
## 3 cellular.fctr 3.192529 0.37614186 FALSE FALSE
## 40 <NA> 6.541176 2.52552391 FALSE FALSE
## 4 <NA> 2.128846 0.16120365 FALSE FALSE
## 22 <NA> 35.372549 0.26867276 FALSE TRUE
## 63 <NA> 1.032491 0.64481462 FALSE FALSE
## 13 <NA> 40.355556 0.10746910 FALSE TRUE
## 6 <NA> 4.006920 0.32240731 FALSE FALSE
## 67 <NA> 1.000000 100.00000000 FALSE FALSE
## 65 <NA> 2.807692 30.14508329 FALSE FALSE
## 10 <NA> 0.000000 0.05373455 TRUE TRUE
## 12 <NA> 0.000000 0.05373455 TRUE TRUE
## 15 <NA> 0.000000 0.05373455 TRUE TRUE
## 25 <NA> 0.000000 0.05373455 TRUE TRUE
## 27 <NA> 0.000000 0.05373455 TRUE TRUE
## 28 <NA> 0.000000 0.05373455 TRUE TRUE
## 29 <NA> 0.000000 0.05373455 TRUE TRUE
## 30 <NA> 0.000000 0.05373455 TRUE TRUE
## 31 <NA> 0.000000 0.05373455 TRUE TRUE
## 33 <NA> 0.000000 0.05373455 TRUE TRUE
## 34 <NA> 0.000000 0.05373455 TRUE TRUE
## 35 <NA> 0.000000 0.05373455 TRUE TRUE
## 37 <NA> 0.000000 0.05373455 TRUE TRUE
## 38 <NA> 0.000000 0.05373455 TRUE TRUE
## 43 <NA> 0.000000 0.05373455 TRUE TRUE
## myNearZV is.cor.y.abs.low
## 64 FALSE FALSE
## 2 FALSE FALSE
## 44 FALSE FALSE
## 23 FALSE FALSE
## 57 FALSE FALSE
## 11 FALSE FALSE
## 60 FALSE FALSE
## 50 FALSE TRUE
## 52 FALSE TRUE
## 45 FALSE TRUE
## 51 FALSE TRUE
## 9 FALSE TRUE
## 61 FALSE TRUE
## 62 FALSE TRUE
## 56 FALSE TRUE
## 59 FALSE TRUE
## 49 FALSE TRUE
## 19 FALSE TRUE
## 26 TRUE TRUE
## 1 FALSE FALSE
## 53 FALSE FALSE
## 18 FALSE FALSE
## 39 FALSE FALSE
## 46 FALSE FALSE
## 55 FALSE FALSE
## 21 FALSE FALSE
## 16 FALSE FALSE
## 48 FALSE FALSE
## 5 FALSE FALSE
## 66 FALSE FALSE
## 24 FALSE FALSE
## 32 FALSE FALSE
## 58 FALSE FALSE
## 14 FALSE FALSE
## 47 FALSE FALSE
## 36 FALSE FALSE
## 54 FALSE FALSE
## 20 FALSE FALSE
## 7 FALSE FALSE
## 41 FALSE FALSE
## 17 FALSE FALSE
## 42 FALSE FALSE
## 8 FALSE FALSE
## 3 FALSE FALSE
## 40 FALSE FALSE
## 4 FALSE FALSE
## 22 FALSE FALSE
## 63 FALSE FALSE
## 13 FALSE FALSE
## 6 FALSE FALSE
## 67 FALSE FALSE
## 65 FALSE FALSE
## 10 TRUE NA
## 12 TRUE NA
## 15 TRUE NA
## 25 TRUE NA
## 27 TRUE NA
## 28 TRUE NA
## 29 TRUE NA
## 30 TRUE NA
## 31 TRUE NA
## 33 TRUE NA
## 34 TRUE NA
## 35 TRUE NA
## 37 TRUE NA
## 38 TRUE NA
## 43 TRUE NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 26 D.npnct18.log -0.02149194 0 0.02149194 <NA>
## 10 D.npnct02.log NA 0 NA <NA>
## 12 D.npnct04.log NA 0 NA <NA>
## 15 D.npnct07.log NA 0 NA <NA>
## 25 D.npnct17.log NA 0 NA <NA>
## 27 D.npnct19.log NA 0 NA <NA>
## 28 D.npnct20.log NA 0 NA <NA>
## 29 D.npnct21.log NA 0 NA <NA>
## 30 D.npnct22.log NA 0 NA <NA>
## 31 D.npnct23.log NA 0 NA <NA>
## 33 D.npnct25.log NA 0 NA <NA>
## 34 D.npnct26.log NA 0 NA <NA>
## 35 D.npnct27.log NA 0 NA <NA>
## 37 D.npnct29.log NA 0 NA <NA>
## 38 D.npnct30.log NA 0 NA <NA>
## 43 D.P.http NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 26 1860 0.10746910 FALSE TRUE TRUE TRUE
## 10 0 0.05373455 TRUE TRUE TRUE NA
## 12 0 0.05373455 TRUE TRUE TRUE NA
## 15 0 0.05373455 TRUE TRUE TRUE NA
## 25 0 0.05373455 TRUE TRUE TRUE NA
## 27 0 0.05373455 TRUE TRUE TRUE NA
## 28 0 0.05373455 TRUE TRUE TRUE NA
## 29 0 0.05373455 TRUE TRUE TRUE NA
## 30 0 0.05373455 TRUE TRUE TRUE NA
## 31 0 0.05373455 TRUE TRUE TRUE NA
## 33 0 0.05373455 TRUE TRUE TRUE NA
## 34 0 0.05373455 TRUE TRUE TRUE NA
## 35 0 0.05373455 TRUE TRUE TRUE NA
## 37 0 0.05373455 TRUE TRUE TRUE NA
## 38 0 0.05373455 TRUE TRUE TRUE NA
## 43 0 0.05373455 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold cellular.fctr
## 1446 1001 1595
## D.T.condit D.T.condition D.T.good
## 2455 2385 2463
## D.T.ipad D.T.new D.T.scratch
## 2432 2520 2431
## D.T.screen D.T.this D.T.use
## 2453 2503 2474
## D.T.work D.T.box D.T.excellent
## 2485 2553 2602
## D.T.great D.T.like D.T.used
## 2539 2591 2540
## D.T.veri D.nwrds.log D.nwrds.unq.log
## 2567 1521 1529
## D.sum.TfIdf D.ratio.sum.TfIdf.nwrds D.nchrs.log
## 1529 1529 1521
## D.nuppr.log D.ndgts.log D.npnct01.log
## 1583 2428 2581
## D.npnct03.log D.npnct05.log D.npnct06.log
## 2616 2594 2556
## D.npnct08.log D.npnct09.log D.npnct10.log
## 2583 2643 2650
## D.npnct11.log D.npnct12.log D.npnct13.log
## 2303 2539 1934
## D.npnct14.log D.npnct15.log D.npnct16.log
## 2584 2638 2548
## D.npnct24.log D.npnct28.log D.nstopwrds.log
## 1521 2651 1761
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline
## 0# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 33.746 35.189 1.443
## 9 partition.data.training 6 0 35.189 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=1 - (nrow(glb_newobs_df) * 1.1 / nrow(glb_trnobs_df)))
glb_fitobs_df <- glb_trnobs_df[split, ]
glb_OOBobs_df <- glb_trnobs_df[!split ,]
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: caToolsif (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_vars)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_vars)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_vars)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_vars
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}
# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 67 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## sold.fctr sold.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 64 sold 1.0000000 TRUE 1.0000000 <NA>
## 67 UniqueID -0.1904242 TRUE 0.1904242 <NA>
## sold.fctr sold.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 64 1.163953 0.1074691 FALSE FALSE FALSE FALSE
## 67 1.000000 100.0000000 FALSE FALSE FALSE FALSE
## sold.fctr NA NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 64 NA TRUE NA NA
## 67 NA FALSE TRUE NA
## sold.fctr NA NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "D.npnct18.log" "D.npnct02.log" "D.npnct04.log" "D.npnct07.log"
## [5] "D.npnct17.log" "D.npnct19.log" "D.npnct20.log" "D.npnct21.log"
## [9] "D.npnct22.log" "D.npnct23.log" "D.npnct25.log" "D.npnct26.log"
## [13] "D.npnct27.log" "D.npnct29.log" "D.npnct30.log" "D.P.http"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2659 61print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 1861 76print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 983 76print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 878 76print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 798 76# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 35.189 35.675 0.486
## 10 fit.models 7 0 35.675 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(model_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.5381485 0.4618515
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5381485 0.4618515
## 2 0.5381485 0.4618515
## 3 0.5381485 0.4618515
## 4 0.5381485 0.4618515
## 5 0.5381485 0.4618515
## 6 0.5381485 0.4618515
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 529
## 2 Y 454
## Prediction
## Reference N Y
## N 529 0
## Y 454 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.381485e-01 0.000000e+00 5.063996e-01 5.696684e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 5.130824e-01 2.644740e-100
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5381485 0.4618515
## 2 0.5381485 0.4618515
## 3 0.5381485 0.4618515
## 4 0.5381485 0.4618515
## 5 0.5381485 0.4618515
## 6 0.5381485 0.4618515
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 472
## 2 Y 406
## Prediction
## Reference N Y
## N 472 0
## Y 406 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.375854e-01 0.000000e+00 5.039574e-01 5.709605e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 5.138359e-01 7.407292e-90
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.374 0.004 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.5381485
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5063996 0.5696684 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5375854
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5039574 0.5709605 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.6318720
## 3 0.2 0.6318720
## 4 0.3 0.6318720
## 5 0.4 0.6318720
## 6 0.5 0.4678999
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 529
## 2 Y 454
## Prediction
## Reference N Y
## N 0 529
## Y 0 454
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.618515e-01 0.000000e+00 4.303316e-01 4.936004e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 9.999993e-01 1.268257e-116
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6323988
## 2 0.1 0.6323988
## 3 0.2 0.6323988
## 4 0.3 0.6323988
## 5 0.4 0.6323988
## 6 0.5 0.4419753
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 472
## 2 Y 406
## Prediction
## Reference N Y
## N 0 472
## Y 0 406
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.624146e-01 0.000000e+00 4.290395e-01 4.960426e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 9.999965e-01 3.201162e-104
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.244 0.002 0.5004892
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.631872 0.4618515
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.4303316 0.4936004 0 0.4820959
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6323988 0.4624146
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4290395 0.4960426 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: biddable, startprice"## Loading required package: rpart## Fitting cp = 0.533 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 983
##
## CP nsplit rel error
## 1 0.5330396 0 1
##
## Node number 1: 983 observations
## predicted class=N expected loss=0.4618515 P(node) =1
## class counts: 529 454
## probabilities: 0.538 0.462
##
## n= 983
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 983 454 N (0.5381485 0.4618515) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 529
## 2 Y 454
## Prediction
## Reference N Y
## N 529 0
## Y 454 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.381485e-01 0.000000e+00 5.063996e-01 5.696684e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 5.130824e-01 2.644740e-100
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 472
## 2 Y 406
## Prediction
## Reference N Y
## N 472 0
## Y 406 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.375854e-01 0.000000e+00 5.039574e-01 5.709605e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 5.138359e-01 7.407292e-90
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.rpart rpart biddable, startprice 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.104 0.011 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.5381485
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5063996 0.5696684 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5375854
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5039574 0.5709605 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: biddable, startprice"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 983
##
## CP nsplit rel error
## 1 0.533039648 0 1.0000000
## 2 0.022026432 1 0.4669604
## 3 0.005506608 3 0.4229075
## 4 0.002202643 5 0.4118943
## 5 0.001468429 12 0.3942731
## 6 0.001101322 18 0.3854626
## 7 0.000000000 20 0.3832599
##
## Variable importance
## biddable startprice
## 53 47
##
## Node number 1: 983 observations, complexity param=0.5330396
## predicted class=N expected loss=0.4618515 P(node) =1
## class counts: 529 454
## probabilities: 0.538 0.462
## left son=2 (535 obs) right son=3 (448 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=156.4154, (0 missing)
## startprice < 104.845 to the right, improve=123.8078, (0 missing)
## Surrogate splits:
## startprice < 102.35 to the right, agree=0.751, adj=0.453, (0 split)
##
## Node number 2: 535 observations, complexity param=0.002202643
## predicted class=N expected loss=0.2037383 P(node) =0.5442523
## class counts: 426 109
## probabilities: 0.796 0.204
## left son=4 (322 obs) right son=5 (213 obs)
## Primary splits:
## startprice < 209.425 to the right, improve=5.39944, (0 missing)
##
## Node number 3: 448 observations, complexity param=0.02202643
## predicted class=Y expected loss=0.2299107 P(node) =0.4557477
## class counts: 103 345
## probabilities: 0.230 0.770
## left son=6 (163 obs) right son=7 (285 obs)
## Primary splits:
## startprice < 129.5 to the right, improve=36.53795, (0 missing)
##
## Node number 4: 322 observations
## predicted class=N expected loss=0.1459627 P(node) =0.3275687
## class counts: 275 47
## probabilities: 0.854 0.146
##
## Node number 5: 213 observations, complexity param=0.002202643
## predicted class=N expected loss=0.2910798 P(node) =0.2166836
## class counts: 151 62
## probabilities: 0.709 0.291
## left son=10 (183 obs) right son=11 (30 obs)
## Primary splits:
## startprice < 75.5 to the right, improve=1.413207, (0 missing)
##
## Node number 6: 163 observations, complexity param=0.02202643
## predicted class=Y expected loss=0.4969325 P(node) =0.1658189
## class counts: 81 82
## probabilities: 0.497 0.503
## left son=12 (34 obs) right son=13 (129 obs)
## Primary splits:
## startprice < 385 to the right, improve=7.588588, (0 missing)
##
## Node number 7: 285 observations, complexity param=0.001101322
## predicted class=Y expected loss=0.07719298 P(node) =0.2899288
## class counts: 22 263
## probabilities: 0.077 0.923
## left son=14 (79 obs) right son=15 (206 obs)
## Primary splits:
## startprice < 79.97 to the right, improve=2.186893, (0 missing)
##
## Node number 10: 183 observations, complexity param=0.001468429
## predicted class=N expected loss=0.2677596 P(node) =0.1861648
## class counts: 134 49
## probabilities: 0.732 0.268
## left son=20 (63 obs) right son=21 (120 obs)
## Primary splits:
## startprice < 129.995 to the left, improve=1.147658, (0 missing)
##
## Node number 11: 30 observations, complexity param=0.002202643
## predicted class=N expected loss=0.4333333 P(node) =0.03051882
## class counts: 17 13
## probabilities: 0.567 0.433
## left son=22 (9 obs) right son=23 (21 obs)
## Primary splits:
## startprice < 29.35 to the left, improve=2.669841, (0 missing)
##
## Node number 12: 34 observations
## predicted class=N expected loss=0.2058824 P(node) =0.034588
## class counts: 27 7
## probabilities: 0.794 0.206
##
## Node number 13: 129 observations, complexity param=0.005506608
## predicted class=Y expected loss=0.4186047 P(node) =0.1312309
## class counts: 54 75
## probabilities: 0.419 0.581
## left son=26 (64 obs) right son=27 (65 obs)
## Primary splits:
## startprice < 205.995 to the right, improve=1.098871, (0 missing)
##
## Node number 14: 79 observations, complexity param=0.001101322
## predicted class=Y expected loss=0.1772152 P(node) =0.08036623
## class counts: 14 65
## probabilities: 0.177 0.823
## left son=28 (7 obs) right son=29 (72 obs)
## Primary splits:
## startprice < 84.5 to the left, improve=2.387181, (0 missing)
##
## Node number 15: 206 observations
## predicted class=Y expected loss=0.03883495 P(node) =0.2095626
## class counts: 8 198
## probabilities: 0.039 0.961
##
## Node number 20: 63 observations
## predicted class=N expected loss=0.1904762 P(node) =0.06408952
## class counts: 51 12
## probabilities: 0.810 0.190
##
## Node number 21: 120 observations, complexity param=0.001468429
## predicted class=N expected loss=0.3083333 P(node) =0.1220753
## class counts: 83 37
## probabilities: 0.692 0.308
## left son=42 (88 obs) right son=43 (32 obs)
## Primary splits:
## startprice < 152.5 to the right, improve=2.245833, (0 missing)
##
## Node number 22: 9 observations
## predicted class=N expected loss=0.1111111 P(node) =0.009155646
## class counts: 8 1
## probabilities: 0.889 0.111
##
## Node number 23: 21 observations, complexity param=0.002202643
## predicted class=Y expected loss=0.4285714 P(node) =0.02136317
## class counts: 9 12
## probabilities: 0.429 0.571
## left son=46 (12 obs) right son=47 (9 obs)
## Primary splits:
## startprice < 57.825 to the right, improve=1.34127, (0 missing)
##
## Node number 26: 64 observations, complexity param=0.005506608
## predicted class=Y expected loss=0.484375 P(node) =0.06510682
## class counts: 31 33
## probabilities: 0.484 0.516
## left son=52 (35 obs) right son=53 (29 obs)
## Primary splits:
## startprice < 287 to the left, improve=1.17072, (0 missing)
##
## Node number 27: 65 observations, complexity param=0.002202643
## predicted class=Y expected loss=0.3538462 P(node) =0.06612411
## class counts: 23 42
## probabilities: 0.354 0.646
## left son=54 (7 obs) right son=55 (58 obs)
## Primary splits:
## startprice < 147 to the left, improve=0.7427814, (0 missing)
##
## Node number 28: 7 observations
## predicted class=N expected loss=0.4285714 P(node) =0.007121058
## class counts: 4 3
## probabilities: 0.571 0.429
##
## Node number 29: 72 observations
## predicted class=Y expected loss=0.1388889 P(node) =0.07324517
## class counts: 10 62
## probabilities: 0.139 0.861
##
## Node number 42: 88 observations
## predicted class=N expected loss=0.25 P(node) =0.08952187
## class counts: 66 22
## probabilities: 0.750 0.250
##
## Node number 43: 32 observations, complexity param=0.001468429
## predicted class=N expected loss=0.46875 P(node) =0.03255341
## class counts: 17 15
## probabilities: 0.531 0.469
## left son=86 (14 obs) right son=87 (18 obs)
## Primary splits:
## startprice < 149.295 to the left, improve=0.6200397, (0 missing)
##
## Node number 46: 12 observations
## predicted class=N expected loss=0.4166667 P(node) =0.01220753
## class counts: 7 5
## probabilities: 0.583 0.417
##
## Node number 47: 9 observations
## predicted class=Y expected loss=0.2222222 P(node) =0.009155646
## class counts: 2 7
## probabilities: 0.222 0.778
##
## Node number 52: 35 observations, complexity param=0.002202643
## predicted class=N expected loss=0.4285714 P(node) =0.03560529
## class counts: 20 15
## probabilities: 0.571 0.429
## left son=104 (10 obs) right son=105 (25 obs)
## Primary splits:
## startprice < 234.485 to the left, improve=0.4628571, (0 missing)
##
## Node number 53: 29 observations
## predicted class=Y expected loss=0.3793103 P(node) =0.02950153
## class counts: 11 18
## probabilities: 0.379 0.621
##
## Node number 54: 7 observations
## predicted class=N expected loss=0.4285714 P(node) =0.007121058
## class counts: 4 3
## probabilities: 0.571 0.429
##
## Node number 55: 58 observations, complexity param=0.001468429
## predicted class=Y expected loss=0.3275862 P(node) =0.05900305
## class counts: 19 39
## probabilities: 0.328 0.672
## left son=110 (47 obs) right son=111 (11 obs)
## Primary splits:
## startprice < 149.995 to the right, improve=0.5768692, (0 missing)
##
## Node number 86: 14 observations
## predicted class=N expected loss=0.3571429 P(node) =0.01424212
## class counts: 9 5
## probabilities: 0.643 0.357
##
## Node number 87: 18 observations
## predicted class=Y expected loss=0.4444444 P(node) =0.01831129
## class counts: 8 10
## probabilities: 0.444 0.556
##
## Node number 104: 10 observations
## predicted class=N expected loss=0.3 P(node) =0.01017294
## class counts: 7 3
## probabilities: 0.700 0.300
##
## Node number 105: 25 observations, complexity param=0.002202643
## predicted class=N expected loss=0.48 P(node) =0.02543235
## class counts: 13 12
## probabilities: 0.520 0.480
## left son=210 (9 obs) right son=211 (16 obs)
## Primary splits:
## startprice < 257.495 to the right, improve=0.605, (0 missing)
##
## Node number 110: 47 observations, complexity param=0.001468429
## predicted class=Y expected loss=0.3617021 P(node) =0.04781282
## class counts: 17 30
## probabilities: 0.362 0.638
## left son=220 (24 obs) right son=221 (23 obs)
## Primary splits:
## startprice < 182.5 to the left, improve=0.9158958, (0 missing)
##
## Node number 111: 11 observations
## predicted class=Y expected loss=0.1818182 P(node) =0.01119023
## class counts: 2 9
## probabilities: 0.182 0.818
##
## Node number 210: 9 observations
## predicted class=N expected loss=0.3333333 P(node) =0.009155646
## class counts: 6 3
## probabilities: 0.667 0.333
##
## Node number 211: 16 observations
## predicted class=Y expected loss=0.4375 P(node) =0.0162767
## class counts: 7 9
## probabilities: 0.438 0.562
##
## Node number 220: 24 observations, complexity param=0.001468429
## predicted class=Y expected loss=0.4583333 P(node) =0.02441506
## class counts: 11 13
## probabilities: 0.458 0.542
## left son=440 (8 obs) right son=441 (16 obs)
## Primary splits:
## startprice < 164.995 to the right, improve=0.6666667, (0 missing)
##
## Node number 221: 23 observations
## predicted class=Y expected loss=0.2608696 P(node) =0.02339776
## class counts: 6 17
## probabilities: 0.261 0.739
##
## Node number 440: 8 observations
## predicted class=N expected loss=0.375 P(node) =0.008138352
## class counts: 5 3
## probabilities: 0.625 0.375
##
## Node number 441: 16 observations
## predicted class=Y expected loss=0.375 P(node) =0.0162767
## class counts: 6 10
## probabilities: 0.375 0.625
##
## n= 983
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 983 454 N (0.53814852 0.46185148)
## 2) biddable< 0.5 535 109 N (0.79626168 0.20373832)
## 4) startprice>=209.425 322 47 N (0.85403727 0.14596273) *
## 5) startprice< 209.425 213 62 N (0.70892019 0.29107981)
## 10) startprice>=75.5 183 49 N (0.73224044 0.26775956)
## 20) startprice< 129.995 63 12 N (0.80952381 0.19047619) *
## 21) startprice>=129.995 120 37 N (0.69166667 0.30833333)
## 42) startprice>=152.5 88 22 N (0.75000000 0.25000000) *
## 43) startprice< 152.5 32 15 N (0.53125000 0.46875000)
## 86) startprice< 149.295 14 5 N (0.64285714 0.35714286) *
## 87) startprice>=149.295 18 8 Y (0.44444444 0.55555556) *
## 11) startprice< 75.5 30 13 N (0.56666667 0.43333333)
## 22) startprice< 29.35 9 1 N (0.88888889 0.11111111) *
## 23) startprice>=29.35 21 9 Y (0.42857143 0.57142857)
## 46) startprice>=57.825 12 5 N (0.58333333 0.41666667) *
## 47) startprice< 57.825 9 2 Y (0.22222222 0.77777778) *
## 3) biddable>=0.5 448 103 Y (0.22991071 0.77008929)
## 6) startprice>=129.5 163 81 Y (0.49693252 0.50306748)
## 12) startprice>=385 34 7 N (0.79411765 0.20588235) *
## 13) startprice< 385 129 54 Y (0.41860465 0.58139535)
## 26) startprice>=205.995 64 31 Y (0.48437500 0.51562500)
## 52) startprice< 287 35 15 N (0.57142857 0.42857143)
## 104) startprice< 234.485 10 3 N (0.70000000 0.30000000) *
## 105) startprice>=234.485 25 12 N (0.52000000 0.48000000)
## 210) startprice>=257.495 9 3 N (0.66666667 0.33333333) *
## 211) startprice< 257.495 16 7 Y (0.43750000 0.56250000) *
## 53) startprice>=287 29 11 Y (0.37931034 0.62068966) *
## 27) startprice< 205.995 65 23 Y (0.35384615 0.64615385)
## 54) startprice< 147 7 3 N (0.57142857 0.42857143) *
## 55) startprice>=147 58 19 Y (0.32758621 0.67241379)
## 110) startprice>=149.995 47 17 Y (0.36170213 0.63829787)
## 220) startprice< 182.5 24 11 Y (0.45833333 0.54166667)
## 440) startprice>=164.995 8 3 N (0.62500000 0.37500000) *
## 441) startprice< 164.995 16 6 Y (0.37500000 0.62500000) *
## 221) startprice>=182.5 23 6 Y (0.26086957 0.73913043) *
## 111) startprice< 149.995 11 2 Y (0.18181818 0.81818182) *
## 7) startprice< 129.5 285 22 Y (0.07719298 0.92280702)
## 14) startprice>=79.97 79 14 Y (0.17721519 0.82278481)
## 28) startprice< 84.5 7 3 N (0.57142857 0.42857143) *
## 29) startprice>=84.5 72 10 Y (0.13888889 0.86111111) *
## 15) startprice< 79.97 206 8 Y (0.03883495 0.96116505) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.6318720
## 3 0.2 0.7555129
## 4 0.3 0.7926167
## 5 0.4 0.7977273
## 6 0.5 0.7962529
## 7 0.6 0.7829268
## 8 0.7 0.7561290
## 9 0.8 0.7240915
## 10 0.9 0.6000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 454
## 2 Y 103
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 75
## 2 351
## Prediction
## Reference N Y
## N 454 75
## Y 103 351
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.189217e-01 6.341232e-01 7.933969e-01 8.425192e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 1.758040e-76 4.299737e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6323988
## 2 0.1 0.6323988
## 3 0.2 0.7243243
## 4 0.3 0.7374847
## 5 0.4 0.7380645
## 6 0.5 0.7243675
## 7 0.6 0.7117241
## 8 0.7 0.6883309
## 9 0.8 0.6697248
## 10 0.9 0.5694683
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 389
## 2 Y 120
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 83
## 2 286
## Prediction
## Reference N Y
## N 389 83
## Y 120 286
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.687927e-01 5.319756e-01 7.394596e-01 7.963092e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 8.504786e-46 1.151383e-02
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart biddable, startprice
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.461 0.008
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8677477 0.4 0.7977273 0.8189217
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7933969 0.8425192 0.6341232 0.815811
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7380645 0.7687927
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7394596 0.7963092 0.5319756if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable, startprice"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00551 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 983
##
## CP nsplit rel error
## 1 0.533039648 0 1.0000000
## 2 0.022026432 1 0.4669604
## 3 0.005506608 3 0.4229075
##
## Variable importance
## biddable startprice
## 58 42
##
## Node number 1: 983 observations, complexity param=0.5330396
## predicted class=N expected loss=0.4618515 P(node) =1
## class counts: 529 454
## probabilities: 0.538 0.462
## left son=2 (535 obs) right son=3 (448 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=156.4154, (0 missing)
## startprice < 104.845 to the right, improve=123.8078, (0 missing)
## Surrogate splits:
## startprice < 102.35 to the right, agree=0.751, adj=0.453, (0 split)
##
## Node number 2: 535 observations
## predicted class=N expected loss=0.2037383 P(node) =0.5442523
## class counts: 426 109
## probabilities: 0.796 0.204
##
## Node number 3: 448 observations, complexity param=0.02202643
## predicted class=Y expected loss=0.2299107 P(node) =0.4557477
## class counts: 103 345
## probabilities: 0.230 0.770
## left son=6 (163 obs) right son=7 (285 obs)
## Primary splits:
## startprice < 129.5 to the right, improve=36.53795, (0 missing)
##
## Node number 6: 163 observations, complexity param=0.02202643
## predicted class=Y expected loss=0.4969325 P(node) =0.1658189
## class counts: 81 82
## probabilities: 0.497 0.503
## left son=12 (34 obs) right son=13 (129 obs)
## Primary splits:
## startprice < 385 to the right, improve=7.588588, (0 missing)
##
## Node number 7: 285 observations
## predicted class=Y expected loss=0.07719298 P(node) =0.2899288
## class counts: 22 263
## probabilities: 0.077 0.923
##
## Node number 12: 34 observations
## predicted class=N expected loss=0.2058824 P(node) =0.034588
## class counts: 27 7
## probabilities: 0.794 0.206
##
## Node number 13: 129 observations
## predicted class=Y expected loss=0.4186047 P(node) =0.1312309
## class counts: 54 75
## probabilities: 0.419 0.581
##
## n= 983
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 983 454 N (0.53814852 0.46185148)
## 2) biddable< 0.5 535 109 N (0.79626168 0.20373832) *
## 3) biddable>=0.5 448 103 Y (0.22991071 0.77008929)
## 6) startprice>=129.5 163 81 Y (0.49693252 0.50306748)
## 12) startprice>=385 34 7 N (0.79411765 0.20588235) *
## 13) startprice< 385 129 54 Y (0.41860465 0.58139535) *
## 7) startprice< 129.5 285 22 Y (0.07719298 0.92280702) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.6318720
## 3 0.2 0.6318720
## 4 0.3 0.7788018
## 5 0.4 0.7788018
## 6 0.5 0.7788018
## 7 0.6 0.7117727
## 8 0.7 0.7117727
## 9 0.8 0.7117727
## 10 0.9 0.7117727
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 453
## 2 Y 116
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 76
## 2 338
## Prediction
## Reference N Y
## N 453 76
## Y 116 338
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.046796e-01 6.046022e-01 7.784902e-01 8.290291e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 1.609558e-68 4.884063e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6323988
## 2 0.1 0.6323988
## 3 0.2 0.6323988
## 4 0.3 0.7352185
## 5 0.4 0.7352185
## 6 0.5 0.7352185
## 7 0.6 0.6798780
## 8 0.7 0.6798780
## 9 0.8 0.6798780
## 10 0.9 0.6798780
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 386
## 2 Y 120
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 86
## 2 286
## Prediction
## Reference N Y
## N 386 86
## Y 120 286
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.653759e-01 5.253058e-01 7.359120e-01 7.930464e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 1.943674e-44 2.149251e-02
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart biddable, startprice 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.04 0.013 0.8266262
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.7788018 0.787423
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7784902 0.8290291 0.5681744 0.7975651
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7352185 0.7653759
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.735912 0.7930464 0.5253058
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01660369 0.03521871# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: biddable, startprice"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0974 -0.7404 -0.3093 0.6197 2.6536
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0009386 0.1710860 -0.005 0.996
## biddable 2.0884307 0.1673591 12.479 <2e-16 ***
## startprice -0.0055838 0.0006065 -9.207 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1357.00 on 982 degrees of freedom
## Residual deviance: 920.02 on 980 degrees of freedom
## AIC: 926.02
##
## Number of Fisher Scoring iterations: 4
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.6676783
## 3 0.2 0.7169150
## 4 0.3 0.7541955
## 5 0.4 0.7774086
## 6 0.5 0.7768786
## 7 0.6 0.7713951
## 8 0.7 0.7580247
## 9 0.8 0.7100271
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 431
## 2 Y 103
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 98
## 2 351
## Prediction
## Reference N Y
## N 431 98
## Y 103 351
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.955239e-01 5.883319e-01 7.689343e-01 8.203298e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 1.079274e-63 7.778375e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6323988
## 2 0.1 0.6644010
## 3 0.2 0.6988417
## 4 0.3 0.7220377
## 5 0.4 0.7422935
## 6 0.5 0.7335907
## 7 0.6 0.7327249
## 8 0.7 0.7180193
## 9 0.8 0.6798780
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 368
## 2 Y 105
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 104
## 2 301
## Prediction
## Reference N Y
## N 368 104
## Y 105 301
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.619590e-01 5.211301e-01 7.323670e-01 7.897810e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 4.196905e-43 1.000000e+00
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm biddable, startprice 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.931 0.013 0.8564097
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.7774086 0.8057097
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7689343 0.8203298 0.606776 0.8257233
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7422935 0.761959
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.732367 0.789781 0.5211301 926.0247
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007139753 0.01292218if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: biddable, startprice, biddable:D.nwrds.unq.log, biddable:D.T.like, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nwrds.log, biddable:D.nuppr.log, biddable:cellular.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1823 -0.7353 -0.3044 0.5772 2.6684
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0200526 0.1723122 0.116 0.90736
## biddable 3.8214400 1.4207524 2.690 0.00715
## startprice -0.0056825 0.0006159 -9.226 < 2e-16
## `biddable:D.nwrds.unq.log` -1.0521896 1.7039816 -0.617 0.53691
## `biddable:D.T.like` -0.8878216 0.6508207 -1.364 0.17252
## `biddable:D.npnct06.log` 0.1070232 0.9263581 0.116 0.90802
## `biddable:D.ratio.nstopwrds.nwrds` -1.4703987 1.4247392 -1.032 0.30205
## `biddable:D.nwrds.log` 0.4447794 1.2233568 0.364 0.71618
## `biddable:D.nuppr.log` -0.0722067 0.2645704 -0.273 0.78491
## `biddable:cellular.fctr1` -0.0812865 0.2994028 -0.271 0.78601
## `biddable:cellular.fctrUnknown` -1.0995856 0.3673900 -2.993 0.00276
##
## (Intercept)
## biddable **
## startprice ***
## `biddable:D.nwrds.unq.log`
## `biddable:D.T.like`
## `biddable:D.npnct06.log`
## `biddable:D.ratio.nstopwrds.nwrds`
## `biddable:D.nwrds.log`
## `biddable:D.nuppr.log`
## `biddable:cellular.fctr1`
## `biddable:cellular.fctrUnknown` **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1357.00 on 982 degrees of freedom
## Residual deviance: 906.12 on 972 degrees of freedom
## AIC: 928.12
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.6681853
## 3 0.2 0.7188049
## 4 0.3 0.7554241
## 5 0.4 0.7786429
## 6 0.5 0.7795824
## 7 0.6 0.7697842
## 8 0.7 0.7537313
## 9 0.8 0.6905089
## 10 0.9 0.3042672
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 457
## 2 Y 118
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 72
## 2 336
## Prediction
## Reference N Y
## N 457 72
## Y 118 336
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.067141e-01 6.083518e-01 7.806165e-01 8.309595e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 1.269559e-69 1.096021e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6323988
## 2 0.1 0.6649660
## 3 0.2 0.6995169
## 4 0.3 0.7222222
## 5 0.4 0.7432099
## 6 0.5 0.7394095
## 7 0.6 0.7203166
## 8 0.7 0.7103064
## 9 0.8 0.6329114
## 10 0.9 0.2575107
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 369
## 2 Y 105
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 103
## 2 301
## Prediction
## Reference N Y
## N 369 103
## Y 105 301
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.630979e-01 5.233392e-01 7.335484e-01 7.908698e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 1.516647e-43 9.447210e-01
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats
## 1 biddable, startprice, biddable:D.nwrds.unq.log, biddable:D.T.like, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nwrds.log, biddable:D.nuppr.log, biddable:cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.943 0.016
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8574444 0.5 0.7795824 0.802664
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7806165 0.8309595 0.6002968 0.8211155
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7432099 0.7630979
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7335484 0.7908698 0.5233392 928.1232
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008656314 0.0161612# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: biddable, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice"
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4255 -0.6594 -0.1486 0.5486 3.5226
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.134e+00 3.737e-01 3.034
## biddable 1.701e+00 2.057e-01 8.270
## D.npnct15.log 1.120e+00 8.548e-01 1.310
## D.T.screen 9.447e-02 6.634e-01 0.142
## D.npnct03.log 3.521e+00 1.990e+00 1.770
## D.T.used -8.930e-01 4.831e-01 -1.848
## D.T.excellent -2.742e-01 3.859e-01 -0.711
## D.T.great -6.345e-01 5.546e-01 -1.144
## D.ratio.sum.TfIdf.nwrds 6.557e-01 2.941e-01 2.229
## D.T.good -5.524e-01 6.682e-01 -0.827
## D.npnct01.log 3.820e-01 6.930e-01 0.551
## D.T.veri -8.662e-01 6.094e-01 -1.421
## D.T.work -1.078e-01 6.824e-01 -0.158
## D.T.scratch -2.516e-01 7.576e-01 -0.332
## D.T.use 1.813e+00 7.940e-01 2.283
## D.T.condition 3.486e-01 7.148e-01 0.488
## D.npnct11.log 3.354e-01 3.456e-01 0.970
## .rnorm -8.793e-02 9.569e-02 -0.919
## D.T.ipad -1.350e+00 8.883e-01 -1.520
## D.npnct10.log 1.362e+00 1.701e+00 0.801
## D.npnct13.log -7.527e-02 3.021e-01 -0.249
## D.npnct08.log -8.044e-02 6.994e-01 -0.115
## D.T.condit -1.943e-01 5.155e-01 -0.377
## color.fctrBlack 1.108e-01 2.502e-01 0.443
## color.fctrGold 5.258e-02 5.690e-01 0.092
## `color.fctrSpace Gray` 2.408e-01 3.599e-01 0.669
## color.fctrWhite -1.772e-01 2.536e-01 -0.699
## storage.fctr128 1.418e+00 5.758e-01 2.464
## storage.fctr32 -1.190e-02 2.659e-01 -0.045
## storage.fctr64 1.482e-01 2.870e-01 0.516
## storage.fctrUnknown 1.058e+00 5.121e-01 2.067
## D.T.this 9.665e-01 9.833e-01 0.983
## D.npnct06.log -3.165e+00 1.642e+00 -1.928
## D.T.box -1.602e+00 8.457e-01 -1.895
## D.npnct28.log -3.671e+00 8.248e+02 -0.004
## D.T.like -1.891e+00 7.275e-01 -2.599
## D.npnct12.log 3.884e-01 7.025e-01 0.553
## D.npnct09.log -7.185e+00 5.742e+02 -0.013
## D.ndgts.log -2.682e-03 4.245e-01 -0.006
## D.nuppr.log -2.309e-01 1.821e-01 -1.268
## cellular.fctr1 -2.198e-01 2.283e-01 -0.962
## cellular.fctrUnknown -9.853e-01 4.658e-01 -2.115
## D.npnct14.log -1.270e+00 9.196e-01 -1.381
## `productline.fctriPad 1` -3.632e-01 3.437e-01 -1.057
## `productline.fctriPad 3` 7.807e-01 3.953e-01 1.975
## `productline.fctriPad 4` 6.152e-01 4.229e-01 1.455
## `productline.fctriPad 5` 1.558e+01 1.455e+03 0.011
## `productline.fctriPad Air` 1.037e+00 4.546e-01 2.281
## `productline.fctriPad Air 2` 2.460e+00 5.773e-01 4.261
## `productline.fctriPad mini` -6.236e-02 3.431e-01 -0.182
## `productline.fctriPad mini 2` 1.505e+00 4.901e-01 3.070
## `productline.fctriPad mini 3` 1.719e+00 5.812e-01 2.957
## `productline.fctriPad mini Retina` 9.038e-01 1.570e+00 0.576
## productline.fctrUnknown -5.391e-01 4.496e-01 -1.199
## D.npnct05.log -2.273e+00 1.496e+00 -1.519
## `condition.fctrFor parts or not working` -8.678e-01 3.474e-01 -2.498
## `condition.fctrManufacturer refurbished` 5.346e-01 7.991e-01 0.669
## condition.fctrNew 1.271e-01 3.396e-01 0.374
## `condition.fctrNew other (see details)` 6.418e-01 4.602e-01 1.395
## `condition.fctrSeller refurbished` -9.658e-01 4.337e-01 -2.227
## startprice -1.167e-02 1.343e-03 -8.688
## Pr(>|z|)
## (Intercept) 0.00241 **
## biddable < 2e-16 ***
## D.npnct15.log 0.19017
## D.T.screen 0.88675
## D.npnct03.log 0.07675 .
## D.T.used 0.06457 .
## D.T.excellent 0.47736
## D.T.great 0.25253
## D.ratio.sum.TfIdf.nwrds 0.02580 *
## D.T.good 0.40839
## D.npnct01.log 0.58151
## D.T.veri 0.15519
## D.T.work 0.87445
## D.T.scratch 0.73987
## D.T.use 0.02244 *
## D.T.condition 0.62581
## D.npnct11.log 0.33183
## .rnorm 0.35819
## D.T.ipad 0.12859
## D.npnct10.log 0.42317
## D.npnct13.log 0.80324
## D.npnct08.log 0.90843
## D.T.condit 0.70618
## color.fctrBlack 0.65783
## color.fctrGold 0.92637
## `color.fctrSpace Gray` 0.50345
## color.fctrWhite 0.48454
## storage.fctr128 0.01375 *
## storage.fctr32 0.96432
## storage.fctr64 0.60573
## storage.fctrUnknown 0.03877 *
## D.T.this 0.32565
## D.npnct06.log 0.05388 .
## D.T.box 0.05813 .
## D.npnct28.log 0.99645
## D.T.like 0.00934 **
## D.npnct12.log 0.58037
## D.npnct09.log 0.99002
## D.ndgts.log 0.99496
## D.nuppr.log 0.20472
## cellular.fctr1 0.33586
## cellular.fctrUnknown 0.03439 *
## D.npnct14.log 0.16722
## `productline.fctriPad 1` 0.29062
## `productline.fctriPad 3` 0.04824 *
## `productline.fctriPad 4` 0.14573
## `productline.fctriPad 5` 0.99146
## `productline.fctriPad Air` 0.02256 *
## `productline.fctriPad Air 2` 2.03e-05 ***
## `productline.fctriPad mini` 0.85580
## `productline.fctriPad mini 2` 0.00214 **
## `productline.fctriPad mini 3` 0.00311 **
## `productline.fctriPad mini Retina` 0.56476
## productline.fctrUnknown 0.23045
## D.npnct05.log 0.12883
## `condition.fctrFor parts or not working` 0.01250 *
## `condition.fctrManufacturer refurbished` 0.50352
## condition.fctrNew 0.70817
## `condition.fctrNew other (see details)` 0.16312
## `condition.fctrSeller refurbished` 0.02595 *
## startprice < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1357.00 on 982 degrees of freedom
## Residual deviance: 799.58 on 922 degrees of freedom
## AIC: 921.58
##
## Number of Fisher Scoring iterations: 14
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.7078652
## 3 0.2 0.7520143
## 4 0.3 0.7869177
## 5 0.4 0.7900969
## 6 0.5 0.8009153
## 7 0.6 0.7985782
## 8 0.7 0.7850000
## 9 0.8 0.7222982
## 10 0.9 0.5008183
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 459
## 2 Y 104
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 70
## 2 350
## Prediction
## Reference N Y
## N 459 70
## Y 104 350
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.229908e-01 6.420083e-01 7.976659e-01 8.463635e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 7.327138e-79 1.235904e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6323988
## 2 0.1 0.6943192
## 3 0.2 0.7048193
## 4 0.3 0.7139738
## 5 0.4 0.7306792
## 6 0.5 0.7252196
## 7 0.6 0.7358739
## 8 0.7 0.7252125
## 9 0.8 0.6542056
## 10 0.9 0.4269663
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 397
## 2 Y 126
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 75
## 2 280
## Prediction
## Reference N Y
## N 397 75
## Y 126 280
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.710706e-01 5.354595e-01 7.418260e-01 7.984830e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 1.022980e-46 4.207286e-04
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 biddable, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.345 0.13
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8936277 0.5 0.8009153 0.787423
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7976659 0.8463635 0.5705824 0.8216216
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7358739 0.7710706
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.741826 0.798483 0.5354595 921.5765
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01660369 0.03260792rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 35.675 60.743 25.069
## 11 fit.models 7 1 60.744 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 64.534 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 64.534 64.548 0.015
## 2 fit.models_1_glm 2 0 64.549 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice"
## Aggregating results
## Fitting final model on full training set## Warning: not plotting observations with leverage one:
## 581
## Warning: not plotting observations with leverage one:
## 581
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4432 -0.6443 -0.1340 0.5521 3.5484
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 3.820e+00 4.840e+00 0.789
## biddable 1.700e+00 2.104e-01 8.080
## D.ratio.nstopwrds.nwrds -2.737e+00 4.831e+00 -0.567
## D.npnct15.log 1.393e+00 1.090e+00 1.278
## D.T.screen 2.995e-01 7.398e-01 0.405
## D.npnct03.log 2.093e+00 2.136e+00 0.980
## D.T.used -1.015e+00 5.694e-01 -1.782
## D.T.excellent 3.108e-01 5.137e-01 0.605
## D.T.great -4.089e-01 5.989e-01 -0.683
## D.ratio.sum.TfIdf.nwrds -3.648e-01 8.711e-01 -0.419
## D.T.good -3.312e-01 7.248e-01 -0.457
## D.npnct01.log 1.232e-01 6.977e-01 0.177
## D.T.veri -8.868e-01 6.298e-01 -1.408
## D.T.work 1.293e-01 7.457e-01 0.173
## D.T.scratch -2.346e-01 8.837e-01 -0.265
## D.T.use 1.700e+00 9.209e-01 1.846
## D.T.condition 9.454e-01 8.542e-01 1.107
## D.npnct11.log 3.383e-01 4.001e-01 0.845
## .rnorm -8.992e-02 9.745e-02 -0.923
## D.T.ipad -1.897e+00 9.819e-01 -1.932
## D.npnct10.log 1.543e+00 1.798e+00 0.858
## D.nstopwrds.log 8.913e-01 1.179e+00 0.756
## D.sum.TfIdf 3.993e-01 2.069e-01 1.930
## D.T.new 1.953e-01 1.101e+00 0.177
## D.npnct13.log -2.189e-01 3.948e-01 -0.555
## D.npnct08.log -1.059e-01 7.526e-01 -0.141
## D.T.condit 1.147e-01 6.343e-01 0.181
## color.fctrBlack 2.116e-01 2.575e-01 0.822
## color.fctrGold 7.473e-02 5.801e-01 0.129
## `color.fctrSpace Gray` 2.538e-01 3.690e-01 0.688
## color.fctrWhite -1.907e-01 2.572e-01 -0.741
## storage.fctr128 1.535e+00 6.046e-01 2.540
## storage.fctr32 3.055e-02 2.726e-01 0.112
## storage.fctr64 1.884e-01 2.944e-01 0.640
## storage.fctrUnknown 1.160e+00 5.208e-01 2.227
## D.npnct16.log 1.568e+00 2.416e+00 0.649
## D.npnct24.log 5.388e+00 7.589e+00 0.710
## D.T.this 7.910e-01 1.052e+00 0.752
## D.npnct06.log -3.938e+00 2.909e+00 -1.354
## D.T.box -1.861e+00 9.200e-01 -2.023
## D.npnct28.log -2.876e+00 1.377e+03 -0.002
## D.T.like -2.207e+00 1.122e+00 -1.968
## D.npnct12.log 1.823e-01 7.157e-01 0.255
## D.nchrs.log -2.694e+00 2.063e+00 -1.306
## D.nwrds.log 1.286e+00 2.150e+00 0.598
## D.npnct09.log -8.098e+00 9.814e+02 -0.008
## D.nwrds.unq.log -2.004e-01 1.098e+00 -0.183
## D.ndgts.log -1.734e-02 4.668e-01 -0.037
## `carrier.fctrAT&T` 1.678e+01 1.066e+03 0.016
## carrier.fctrOther 3.283e+01 1.772e+03 0.019
## carrier.fctrSprint 1.725e+01 1.066e+03 0.016
## `carrier.fctrT-Mobile` 1.643e+01 1.066e+03 0.015
## carrier.fctrUnknown 1.665e+01 1.066e+03 0.016
## carrier.fctrVerizon 1.680e+01 1.066e+03 0.016
## D.nuppr.log -1.826e-01 2.998e-01 -0.609
## cellular.fctr1 -1.697e+01 1.066e+03 -0.016
## cellular.fctrUnknown -1.752e+01 1.066e+03 -0.016
## D.npnct14.log -1.582e+00 9.542e-01 -1.658
## `productline.fctriPad 1` -3.825e-01 3.555e-01 -1.076
## `productline.fctriPad 3` 8.347e-01 4.038e-01 2.067
## `productline.fctriPad 4` 6.736e-01 4.422e-01 1.523
## `productline.fctriPad 5` 6.013e-01 2.786e+03 0.000
## `productline.fctriPad Air` 1.124e+00 4.834e-01 2.326
## `productline.fctriPad Air 2` 2.613e+00 5.943e-01 4.396
## `productline.fctriPad mini` -2.327e-02 3.520e-01 -0.066
## `productline.fctriPad mini 2` 1.504e+00 5.136e-01 2.928
## `productline.fctriPad mini 3` 1.845e+00 5.935e-01 3.109
## `productline.fctriPad mini Retina` 7.571e-01 1.692e+00 0.448
## productline.fctrUnknown -6.318e-01 4.595e-01 -1.375
## D.npnct05.log -2.254e+00 1.592e+00 -1.416
## `condition.fctrFor parts or not working` -1.054e+00 3.563e-01 -2.959
## `condition.fctrManufacturer refurbished` 6.416e-01 8.128e-01 0.789
## condition.fctrNew 1.465e-01 3.453e-01 0.424
## `condition.fctrNew other (see details)` 6.349e-01 4.897e-01 1.297
## `condition.fctrSeller refurbished` -8.435e-01 4.364e-01 -1.933
## startprice -1.197e-02 1.388e-03 -8.628
## Pr(>|z|)
## (Intercept) 0.42995
## biddable 6.5e-16 ***
## D.ratio.nstopwrds.nwrds 0.57104
## D.npnct15.log 0.20122
## D.T.screen 0.68565
## D.npnct03.log 0.32713
## D.T.used 0.07473 .
## D.T.excellent 0.54519
## D.T.great 0.49470
## D.ratio.sum.TfIdf.nwrds 0.67536
## D.T.good 0.64769
## D.npnct01.log 0.85984
## D.T.veri 0.15907
## D.T.work 0.86231
## D.T.scratch 0.79064
## D.T.use 0.06485 .
## D.T.condition 0.26835
## D.npnct11.log 0.39787
## .rnorm 0.35617
## D.T.ipad 0.05331 .
## D.npnct10.log 0.39102
## D.nstopwrds.log 0.44948
## D.sum.TfIdf 0.05363 .
## D.T.new 0.85926
## D.npnct13.log 0.57923
## D.npnct08.log 0.88809
## D.T.condit 0.85648
## color.fctrBlack 0.41122
## color.fctrGold 0.89750
## `color.fctrSpace Gray` 0.49166
## color.fctrWhite 0.45846
## storage.fctr128 0.01110 *
## storage.fctr32 0.91075
## storage.fctr64 0.52214
## storage.fctrUnknown 0.02593 *
## D.npnct16.log 0.51636
## D.npnct24.log 0.47771
## D.T.this 0.45209
## D.npnct06.log 0.17577
## D.T.box 0.04309 *
## D.npnct28.log 0.99833
## D.T.like 0.04909 *
## D.npnct12.log 0.79891
## D.nchrs.log 0.19166
## D.nwrds.log 0.54967
## D.npnct09.log 0.99342
## D.nwrds.unq.log 0.85510
## D.ndgts.log 0.97036
## `carrier.fctrAT&T` 0.98745
## carrier.fctrOther 0.98522
## carrier.fctrSprint 0.98709
## `carrier.fctrT-Mobile` 0.98770
## carrier.fctrUnknown 0.98754
## carrier.fctrVerizon 0.98743
## D.nuppr.log 0.54246
## cellular.fctr1 0.98730
## cellular.fctrUnknown 0.98689
## D.npnct14.log 0.09724 .
## `productline.fctriPad 1` 0.28199
## `productline.fctriPad 3` 0.03871 *
## `productline.fctriPad 4` 0.12768
## `productline.fctriPad 5` 0.99983
## `productline.fctriPad Air` 0.02003 *
## `productline.fctriPad Air 2` 1.1e-05 ***
## `productline.fctriPad mini` 0.94728
## `productline.fctriPad mini 2` 0.00341 **
## `productline.fctriPad mini 3` 0.00188 **
## `productline.fctriPad mini Retina` 0.65443
## productline.fctrUnknown 0.16917
## D.npnct05.log 0.15689
## `condition.fctrFor parts or not working` 0.00308 **
## `condition.fctrManufacturer refurbished` 0.42995
## condition.fctrNew 0.67136
## `condition.fctrNew other (see details)` 0.19480
## `condition.fctrSeller refurbished` 0.05326 .
## startprice < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1357.00 on 982 degrees of freedom
## Residual deviance: 785.89 on 907 degrees of freedom
## AIC: 937.89
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.7146322
## 3 0.2 0.7590580
## 4 0.3 0.7904667
## 5 0.4 0.7900969
## 6 0.5 0.8045714
## 7 0.6 0.8028504
## 8 0.7 0.7845579
## 9 0.8 0.7230559
## 10 0.9 0.5153971
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 460 69
## 2 Y 102 352
## Prediction
## Reference N Y
## N 460 69
## Y 102 352
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.260427e-01 6.482358e-01 8.008706e-01 8.492438e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 1.117363e-80 1.440104e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6323988
## 2 0.1 0.6984698
## 3 0.2 0.7145709
## 4 0.3 0.7258938
## 5 0.4 0.7432591
## 6 0.5 0.7431421
## 7 0.6 0.7315789
## 8 0.7 0.7265077
## 9 0.8 0.6615385
## 10 0.9 0.4440299
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 342 130
## 2 Y 89 317
## Prediction
## Reference N Y
## N 342 130
## Y 89 317
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.505695e-01 5.018214e-01 7.205680e-01 7.788787e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 7.864652e-39 6.872718e-03
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.462 0.195
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8972086 0.5 0.8045714 0.7802875
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8008706 0.8492438 0.5567983 0.8306911
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7432591 0.7505695
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.720568 0.7788787 0.5018214 937.8927
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01340801 0.02595814
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 64.549 69.854 5.305
## 3 fit.models_1_bayesglm 3 0 69.855 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
## Loading required package: lme4
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4303 -0.6534 -0.1522 0.5605 3.4677
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 0.937071 2.091960 0.448
## biddable 1.703716 0.204424 8.334
## D.ratio.nstopwrds.nwrds 0.175284 2.059335 0.085
## D.npnct15.log 0.879466 0.886235 0.992
## D.T.screen 0.172184 0.680012 0.253
## D.npnct03.log 2.522462 1.895013 1.331
## D.T.used -0.744717 0.515315 -1.445
## D.T.excellent 0.016522 0.416915 0.040
## D.T.great -0.425162 0.563190 -0.755
## D.ratio.sum.TfIdf.nwrds 0.162914 0.509521 0.320
## D.T.good -0.285617 0.653384 -0.437
## D.npnct01.log 0.198426 0.676975 0.293
## D.T.veri -0.760489 0.579365 -1.313
## D.T.work -0.044486 0.703734 -0.063
## D.T.scratch -0.293226 0.819011 -0.358
## D.T.use 1.712065 0.843921 2.029
## D.T.condition 0.639269 0.756760 0.845
## D.npnct11.log 0.243735 0.374585 0.651
## .rnorm -0.089355 0.094853 -0.942
## D.T.ipad -1.519099 0.913835 -1.662
## D.npnct10.log 1.506435 1.750646 0.861
## D.nstopwrds.log 0.270510 0.526382 0.514
## D.sum.TfIdf 0.287710 0.164996 1.744
## D.T.new 0.482312 1.074518 0.449
## D.npnct13.log -0.238872 0.364117 -0.656
## D.npnct08.log -0.191340 0.712565 -0.269
## D.T.condit -0.031605 0.567855 -0.056
## color.fctrBlack 0.130805 0.248007 0.527
## color.fctrGold 0.089119 0.542317 0.164
## `color.fctrSpace Gray` 0.247886 0.351594 0.705
## color.fctrWhite -0.192728 0.248780 -0.775
## storage.fctr128 1.307923 0.565527 2.313
## storage.fctr32 -0.031948 0.261857 -0.122
## storage.fctr64 0.145673 0.283170 0.514
## storage.fctrUnknown 0.951992 0.485334 1.962
## D.npnct16.log 0.832696 2.077839 0.401
## D.npnct24.log 0.152843 2.493225 0.061
## D.T.this 0.822634 1.002052 0.821
## D.npnct06.log -3.253666 2.433477 -1.337
## D.T.box -1.755432 0.898280 -1.954
## D.npnct28.log -1.398481 9.118640 -0.153
## D.T.like -1.940256 1.079835 -1.797
## D.npnct12.log 0.279952 0.677773 0.413
## D.nchrs.log -0.216655 0.476760 -0.454
## D.nwrds.log -0.080621 0.698117 -0.115
## D.npnct09.log -1.142154 5.967120 -0.191
## D.nwrds.unq.log -0.289712 0.618633 -0.468
## D.ndgts.log -0.018167 0.419035 -0.043
## `carrier.fctrAT&T` 0.173813 0.722168 0.241
## carrier.fctrOther 1.615166 1.837332 0.879
## carrier.fctrSprint 0.617647 0.982417 0.629
## `carrier.fctrT-Mobile` -0.075141 1.006689 -0.075
## carrier.fctrUnknown 0.154089 0.701345 0.220
## carrier.fctrVerizon 0.176961 0.749910 0.236
## D.nuppr.log -0.208839 0.249578 -0.837
## cellular.fctr1 -0.369343 0.685362 -0.539
## cellular.fctrUnknown -1.008821 0.765605 -1.318
## D.npnct14.log -1.453862 0.895492 -1.624
## `productline.fctriPad 1` -0.412108 0.337113 -1.222
## `productline.fctriPad 3` 0.641517 0.374815 1.712
## `productline.fctriPad 4` 0.511000 0.407442 1.254
## `productline.fctriPad 5` 0.581777 1.967610 0.296
## `productline.fctriPad Air` 0.937362 0.438988 2.135
## `productline.fctriPad Air 2` 2.246081 0.539658 4.162
## `productline.fctriPad mini` -0.149427 0.327597 -0.456
## `productline.fctriPad mini 2` 1.287360 0.472938 2.722
## `productline.fctriPad mini 3` 1.513268 0.542259 2.791
## `productline.fctriPad mini Retina` 0.398660 1.280702 0.311
## productline.fctrUnknown -0.612096 0.428579 -1.428
## D.npnct05.log -1.826474 1.317425 -1.386
## `condition.fctrFor parts or not working` -0.886337 0.342779 -2.586
## `condition.fctrManufacturer refurbished` 0.511012 0.743615 0.687
## condition.fctrNew 0.162843 0.332660 0.490
## `condition.fctrNew other (see details)` 0.607996 0.457064 1.330
## `condition.fctrSeller refurbished` -0.848881 0.417718 -2.032
## startprice -0.011286 0.001284 -8.793
## Pr(>|z|)
## (Intercept) 0.65420
## biddable < 2e-16 ***
## D.ratio.nstopwrds.nwrds 0.93217
## D.npnct15.log 0.32102
## D.T.screen 0.80011
## D.npnct03.log 0.18315
## D.T.used 0.14841
## D.T.excellent 0.96839
## D.T.great 0.45030
## D.ratio.sum.TfIdf.nwrds 0.74917
## D.T.good 0.66201
## D.npnct01.log 0.76944
## D.T.veri 0.18931
## D.T.work 0.94960
## D.T.scratch 0.72032
## D.T.use 0.04249 *
## D.T.condition 0.39825
## D.npnct11.log 0.51525
## .rnorm 0.34617
## D.T.ipad 0.09645 .
## D.npnct10.log 0.38951
## D.nstopwrds.log 0.60732
## D.sum.TfIdf 0.08120 .
## D.T.new 0.65353
## D.npnct13.log 0.51180
## D.npnct08.log 0.78830
## D.T.condit 0.95562
## color.fctrBlack 0.59790
## color.fctrGold 0.86947
## `color.fctrSpace Gray` 0.48079
## color.fctrWhite 0.43852
## storage.fctr128 0.02074 *
## storage.fctr32 0.90289
## storage.fctr64 0.60695
## storage.fctrUnknown 0.04982 *
## D.npnct16.log 0.68860
## D.npnct24.log 0.95112
## D.T.this 0.41168
## D.npnct06.log 0.18121
## D.T.box 0.05068 .
## D.npnct28.log 0.87811
## D.T.like 0.07237 .
## D.npnct12.log 0.67957
## D.nchrs.log 0.64952
## D.nwrds.log 0.90806
## D.npnct09.log 0.84821
## D.nwrds.unq.log 0.63956
## D.ndgts.log 0.96542
## `carrier.fctrAT&T` 0.80980
## carrier.fctrOther 0.37936
## carrier.fctrSprint 0.52954
## `carrier.fctrT-Mobile` 0.94050
## carrier.fctrUnknown 0.82610
## carrier.fctrVerizon 0.81345
## D.nuppr.log 0.40272
## cellular.fctr1 0.58995
## cellular.fctrUnknown 0.18761
## D.npnct14.log 0.10448
## `productline.fctriPad 1` 0.22153
## `productline.fctriPad 3` 0.08698 .
## `productline.fctriPad 4` 0.20978
## `productline.fctriPad 5` 0.76748
## `productline.fctriPad Air` 0.03274 *
## `productline.fctriPad Air 2` 3.15e-05 ***
## `productline.fctriPad mini` 0.64830
## `productline.fctriPad mini 2` 0.00649 **
## `productline.fctriPad mini 3` 0.00526 **
## `productline.fctriPad mini Retina` 0.75559
## productline.fctrUnknown 0.15323
## D.npnct05.log 0.16563
## `condition.fctrFor parts or not working` 0.00972 **
## `condition.fctrManufacturer refurbished` 0.49196
## condition.fctrNew 0.62448
## `condition.fctrNew other (see details)` 0.18345
## `condition.fctrSeller refurbished` 0.04213 *
## startprice < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1357.00 on 982 degrees of freedom
## Residual deviance: 792.58 on 907 degrees of freedom
## AIC: 944.58
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.7095736
## 3 0.2 0.7547170
## 4 0.3 0.7853610
## 5 0.4 0.7870968
## 6 0.5 0.8064147
## 7 0.6 0.7995255
## 8 0.7 0.7820672
## 9 0.8 0.7197802
## 10 0.9 0.4909390
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 462
## 2 Y 102
## sold.fctr.predict.All.X.bayesglm.Y
## 1 67
## 2 352
## Prediction
## Reference N Y
## N 462 67
## Y 102 352
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.280773e-01 6.522408e-01 8.030086e-01 8.511624e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 6.633558e-82 8.912703e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6323988
## 2 0.1 0.6911370
## 3 0.2 0.7066929
## 4 0.3 0.7217391
## 5 0.4 0.7321637
## 6 0.5 0.7359199
## 7 0.6 0.7378129
## 8 0.7 0.7226174
## 9 0.8 0.6530612
## 10 0.9 0.4285714
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 399
## 2 Y 126
## sold.fctr.predict.All.X.bayesglm.Y
## 1 73
## 2 280
## Prediction
## Reference N Y
## N 399 73
## Y 126 280
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.733485e-01 5.399220e-01 7.441936e-01 8.006557e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 1.199284e-47 2.276433e-04
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.249 0.289
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8955306 0.5 0.8064147 0.7833456
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8030086 0.8511624 0.5628973 0.8273409
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7378129 0.7733485
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7441936 0.8006557 0.539922 944.5838
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01251998 0.02395759
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_bayesglm 3 0 69.855 75.363 5.508
## 4 fit.models_1_rpart 4 0 75.364 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0176 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 983
##
## CP nsplit rel error
## 1 0.53303965 0 1.0000000
## 2 0.02202643 1 0.4669604
## 3 0.01762115 3 0.4229075
##
## Variable importance
## biddable
## 50
## startprice
## 37
## D.ratio.sum.TfIdf.nwrds
## 2
## condition.fctrNew
## 2
## productline.fctriPad Air 2
## 2
## D.nchrs.log
## 2
## condition.fctrFor parts or not working
## 1
## D.nwrds.log
## 1
## storage.fctr128
## 1
## productline.fctriPad mini 3
## 1
## color.fctrSpace Gray
## 1
##
## Node number 1: 983 observations, complexity param=0.5330396
## predicted class=N expected loss=0.4618515 P(node) =1
## class counts: 529 454
## probabilities: 0.538 0.462
## left son=2 (535 obs) right son=3 (448 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=156.415400, (0 missing)
## startprice < 104.845 to the right, improve=123.807800, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 11.516540, (0 missing)
## D.nwrds.unq.log < 2.249905 to the right, improve= 11.297800, (0 missing)
## D.nchrs.log < 4.375737 to the right, improve= 9.854373, (0 missing)
## Surrogate splits:
## startprice < 102.35 to the right, agree=0.751, adj=0.453, (0 split)
## D.ratio.sum.TfIdf.nwrds < 0.8613655 to the left, agree=0.564, adj=0.042, (0 split)
## D.nchrs.log < 3.860674 to the right, agree=0.560, adj=0.033, (0 split)
## D.nwrds.log < 2.138333 to the right, agree=0.557, adj=0.029, (0 split)
## condition.fctrFor parts or not working < 0.5 to the left, agree=0.557, adj=0.029, (0 split)
##
## Node number 2: 535 observations
## predicted class=N expected loss=0.2037383 P(node) =0.5442523
## class counts: 426 109
## probabilities: 0.796 0.204
##
## Node number 3: 448 observations, complexity param=0.02202643
## predicted class=Y expected loss=0.2299107 P(node) =0.4557477
## class counts: 103 345
## probabilities: 0.230 0.770
## left son=6 (163 obs) right son=7 (285 obs)
## Primary splits:
## startprice < 129.5 to the right, improve=36.537950, (0 missing)
## carrier.fctrUnknown < 0.5 to the right, improve= 3.611797, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 3.496252, (0 missing)
## cellular.fctrUnknown < 0.5 to the right, improve= 3.192115, (0 missing)
## D.T.box < 0.3724958 to the right, improve= 2.801863, (0 missing)
## Surrogate splits:
## condition.fctrNew < 0.5 to the right, agree=0.696, adj=0.166, (0 split)
## productline.fctriPad Air 2 < 0.5 to the right, agree=0.674, adj=0.104, (0 split)
## storage.fctr128 < 0.5 to the right, agree=0.661, adj=0.067, (0 split)
## color.fctrSpace Gray < 0.5 to the right, agree=0.658, adj=0.061, (0 split)
## productline.fctriPad mini 3 < 0.5 to the right, agree=0.656, adj=0.055, (0 split)
##
## Node number 6: 163 observations, complexity param=0.02202643
## predicted class=Y expected loss=0.4969325 P(node) =0.1658189
## class counts: 81 82
## probabilities: 0.497 0.503
## left son=12 (34 obs) right son=13 (129 obs)
## Primary splits:
## startprice < 385 to the right, improve=7.588588, (0 missing)
## carrier.fctrUnknown < 0.5 to the right, improve=3.465163, (0 missing)
## cellular.fctrUnknown < 0.5 to the right, improve=2.475734, (0 missing)
## storage.fctrUnknown < 0.5 to the right, improve=2.404997, (0 missing)
## productline.fctriPad Air < 0.5 to the left, improve=2.215630, (0 missing)
## Surrogate splits:
## color.fctrGold < 0.5 to the right, agree=0.834, adj=0.206, (0 split)
## productline.fctriPad Air 2 < 0.5 to the right, agree=0.834, adj=0.206, (0 split)
## storage.fctr128 < 0.5 to the right, agree=0.828, adj=0.176, (0 split)
## productline.fctriPad mini 3 < 0.5 to the right, agree=0.804, adj=0.059, (0 split)
## condition.fctrNew < 0.5 to the right, agree=0.804, adj=0.059, (0 split)
##
## Node number 7: 285 observations
## predicted class=Y expected loss=0.07719298 P(node) =0.2899288
## class counts: 22 263
## probabilities: 0.077 0.923
##
## Node number 12: 34 observations
## predicted class=N expected loss=0.2058824 P(node) =0.034588
## class counts: 27 7
## probabilities: 0.794 0.206
##
## Node number 13: 129 observations
## predicted class=Y expected loss=0.4186047 P(node) =0.1312309
## class counts: 54 75
## probabilities: 0.419 0.581
##
## n= 983
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 983 454 N (0.53814852 0.46185148)
## 2) biddable< 0.5 535 109 N (0.79626168 0.20373832) *
## 3) biddable>=0.5 448 103 Y (0.22991071 0.77008929)
## 6) startprice>=129.5 163 81 Y (0.49693252 0.50306748)
## 12) startprice>=385 34 7 N (0.79411765 0.20588235) *
## 13) startprice< 385 129 54 Y (0.41860465 0.58139535) *
## 7) startprice< 129.5 285 22 Y (0.07719298 0.92280702) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.6318720
## 3 0.2 0.6318720
## 4 0.3 0.7788018
## 5 0.4 0.7788018
## 6 0.5 0.7788018
## 7 0.6 0.7117727
## 8 0.7 0.7117727
## 9 0.8 0.7117727
## 10 0.9 0.7117727
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 453
## 2 Y 116
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 76
## 2 338
## Prediction
## Reference N Y
## N 453 76
## Y 116 338
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.046796e-01 6.046022e-01 7.784902e-01 8.290291e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 1.609558e-68 4.884063e-03
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6323988
## 2 0.1 0.6323988
## 3 0.2 0.6323988
## 4 0.3 0.7352185
## 5 0.4 0.7352185
## 6 0.5 0.7352185
## 7 0.6 0.6798780
## 8 0.7 0.6798780
## 9 0.8 0.6798780
## 10 0.9 0.6798780
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 386
## 2 Y 120
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 86
## 2 286
## Prediction
## Reference N Y
## N 386 86
## Y 120 286
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.653759e-01 5.253058e-01 7.359120e-01 7.930464e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 1.943674e-44 2.149251e-02
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.476 0.072
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8266262 0.5 0.7788018 0.7925199
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7784902 0.8290291 0.5770549 0.7975651
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7352185 0.7653759
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.735912 0.7930464 0.5253058
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02059971 0.04214872
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_rpart 4 0 75.364 80.442 5.078
## 5 fit.models_1_rf 5 0 80.442 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 38 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 983 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1966 matrix numeric
## oob.times 983 -none- numeric
## classes 2 -none- character
## importance 74 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 983 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 74 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6318720
## 2 0.1 0.8292237
## 3 0.2 0.9218274
## 4 0.3 0.9721627
## 5 0.4 0.9901639
## 6 0.5 0.9933921
## 7 0.6 0.9832402
## 8 0.7 0.9317647
## 9 0.8 0.8520860
## 10 0.9 0.7612551
## 11 1.0 0.2161100
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 526
## 2 Y 3
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 3
## 2 451
## Prediction
## Reference N Y
## N 526 3
## Y 3 451
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.938962e-01 9.877210e-01 9.867623e-01 9.977568e-01 5.381485e-01
## AccuracyPValue McnemarPValue
## 1.488949e-250 1.000000e+00
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.63239875
## 2 0.1 0.67644521
## 3 0.2 0.71031746
## 4 0.3 0.73859844
## 5 0.4 0.74695864
## 6 0.5 0.73907285
## 7 0.6 0.73115220
## 8 0.7 0.70254111
## 9 0.8 0.64976228
## 10 0.9 0.59900166
## 11 1.0 0.09389671
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 363
## 2 Y 99
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 109
## 2 307
## Prediction
## Reference N Y
## N 363 109
## Y 99 307
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.630979e-01 5.243225e-01 7.335484e-01 7.908698e-01 5.375854e-01
## AccuracyPValue McnemarPValue
## 1.516647e-43 5.326028e-01
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 15.192 4.752
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9997564 0.5 0.9933921 0.7782549
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9867623 0.9977568 0.5516744 0.8374984
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7469586 0.7630979
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7335484 0.7908698 0.5243225
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01159582 0.02333356# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#model_id <- "Rank9.2"; indep_vars_vctr <- c(NULL
# ,"<feat1>"
# ,"<feat1>*<feat2>"
# ,"<feat1>:<feat2>"
# )
# for (method in c("bayesglm")) {
# ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(model_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
# }
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice
## Max.cor.Y.rpart biddable, startprice
## Max.cor.Y.glm biddable, startprice
## Interact.High.cor.Y.glm biddable, startprice, biddable:D.nwrds.unq.log, biddable:D.T.like, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nwrds.log, biddable:D.nuppr.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.374
## Random.myrandom_classfr 0 0.244
## Max.cor.Y.cv.0.rpart 0 1.104
## Max.cor.Y.cv.0.cp.0.rpart 0 0.461
## Max.cor.Y.rpart 3 1.040
## Max.cor.Y.glm 1 0.931
## Interact.High.cor.Y.glm 1 0.943
## Low.cor.X.glm 1 1.345
## All.X.glm 1 1.462
## All.X.bayesglm 1 2.249
## All.X.no.rnorm.rpart 3 1.476
## All.X.no.rnorm.rf 3 15.192
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.004 0.5000000
## Random.myrandom_classfr 0.002 0.5004892
## Max.cor.Y.cv.0.rpart 0.011 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.008 0.8677477
## Max.cor.Y.rpart 0.013 0.8266262
## Max.cor.Y.glm 0.013 0.8564097
## Interact.High.cor.Y.glm 0.016 0.8574444
## Low.cor.X.glm 0.130 0.8936277
## All.X.glm 0.195 0.8972086
## All.X.bayesglm 0.289 0.8955306
## All.X.no.rnorm.rpart 0.072 0.8266262
## All.X.no.rnorm.rf 4.752 0.9997564
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6318720
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.7977273
## Max.cor.Y.rpart 0.5 0.7788018
## Max.cor.Y.glm 0.4 0.7774086
## Interact.High.cor.Y.glm 0.5 0.7795824
## Low.cor.X.glm 0.5 0.8009153
## All.X.glm 0.5 0.8045714
## All.X.bayesglm 0.5 0.8064147
## All.X.no.rnorm.rpart 0.5 0.7788018
## All.X.no.rnorm.rf 0.5 0.9933921
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.5381485 0.5063996
## Random.myrandom_classfr 0.4618515 0.4303316
## Max.cor.Y.cv.0.rpart 0.5381485 0.5063996
## Max.cor.Y.cv.0.cp.0.rpart 0.8189217 0.7933969
## Max.cor.Y.rpart 0.7874230 0.7784902
## Max.cor.Y.glm 0.8057097 0.7689343
## Interact.High.cor.Y.glm 0.8026640 0.7806165
## Low.cor.X.glm 0.7874230 0.7976659
## All.X.glm 0.7802875 0.8008706
## All.X.bayesglm 0.7833456 0.8030086
## All.X.no.rnorm.rpart 0.7925199 0.7784902
## All.X.no.rnorm.rf 0.7782549 0.9867623
## max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5696684 0.0000000 0.5000000
## Random.myrandom_classfr 0.4936004 0.0000000 0.4820959
## Max.cor.Y.cv.0.rpart 0.5696684 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8425192 0.6341232 0.8158110
## Max.cor.Y.rpart 0.8290291 0.5681744 0.7975651
## Max.cor.Y.glm 0.8203298 0.6067760 0.8257233
## Interact.High.cor.Y.glm 0.8309595 0.6002968 0.8211155
## Low.cor.X.glm 0.8463635 0.5705824 0.8216216
## All.X.glm 0.8492438 0.5567983 0.8306911
## All.X.bayesglm 0.8511624 0.5628973 0.8273409
## All.X.no.rnorm.rpart 0.8290291 0.5770549 0.7975651
## All.X.no.rnorm.rf 0.9977568 0.5516744 0.8374984
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6323988
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.7380645
## Max.cor.Y.rpart 0.5 0.7352185
## Max.cor.Y.glm 0.4 0.7422935
## Interact.High.cor.Y.glm 0.4 0.7432099
## Low.cor.X.glm 0.6 0.7358739
## All.X.glm 0.4 0.7432591
## All.X.bayesglm 0.6 0.7378129
## All.X.no.rnorm.rpart 0.5 0.7352185
## All.X.no.rnorm.rf 0.4 0.7469586
## max.Accuracy.OOB max.AccuracyLower.OOB
## MFO.myMFO_classfr 0.5375854 0.5039574
## Random.myrandom_classfr 0.4624146 0.4290395
## Max.cor.Y.cv.0.rpart 0.5375854 0.5039574
## Max.cor.Y.cv.0.cp.0.rpart 0.7687927 0.7394596
## Max.cor.Y.rpart 0.7653759 0.7359120
## Max.cor.Y.glm 0.7619590 0.7323670
## Interact.High.cor.Y.glm 0.7630979 0.7335484
## Low.cor.X.glm 0.7710706 0.7418260
## All.X.glm 0.7505695 0.7205680
## All.X.bayesglm 0.7733485 0.7441936
## All.X.no.rnorm.rpart 0.7653759 0.7359120
## All.X.no.rnorm.rf 0.7630979 0.7335484
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5709605 0.0000000
## Random.myrandom_classfr 0.4960426 0.0000000
## Max.cor.Y.cv.0.rpart 0.5709605 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7963092 0.5319756
## Max.cor.Y.rpart 0.7930464 0.5253058
## Max.cor.Y.glm 0.7897810 0.5211301
## Interact.High.cor.Y.glm 0.7908698 0.5233392
## Low.cor.X.glm 0.7984830 0.5354595
## All.X.glm 0.7788787 0.5018214
## All.X.bayesglm 0.8006557 0.5399220
## All.X.no.rnorm.rpart 0.7930464 0.5253058
## All.X.no.rnorm.rf 0.7908698 0.5243225
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA NA
## Random.myrandom_classfr NA NA NA
## Max.cor.Y.cv.0.rpart NA NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA NA
## Max.cor.Y.rpart 0.016603687 0.03521871 NA
## Max.cor.Y.glm 0.007139753 0.01292218 926.0247
## Interact.High.cor.Y.glm 0.008656314 0.01616120 928.1232
## Low.cor.X.glm 0.016603687 0.03260792 921.5765
## All.X.glm 0.013408008 0.02595814 937.8927
## All.X.bayesglm 0.012519984 0.02395759 944.5838
## All.X.no.rnorm.rpart 0.020599707 0.04214872 NA
## All.X.no.rnorm.rf 0.011595820 0.02333356 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rf 5 0 80.442 99.424 18.982
## 6 fit.models_1_end 6 0 99.425 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 60.744 99.432 38.688
## 12 fit.models 7 2 99.433 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice
## Max.cor.Y.rpart biddable, startprice
## Max.cor.Y.glm biddable, startprice
## Interact.High.cor.Y.glm biddable, startprice, biddable:D.nwrds.unq.log, biddable:D.T.like, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nwrds.log, biddable:D.nuppr.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.5004892
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.8677477
## Max.cor.Y.rpart 3 0.8266262
## Max.cor.Y.glm 1 0.8564097
## Interact.High.cor.Y.glm 1 0.8574444
## Low.cor.X.glm 1 0.8936277
## All.X.glm 1 0.8972086
## All.X.bayesglm 1 0.8955306
## All.X.no.rnorm.rpart 3 0.8266262
## All.X.no.rnorm.rf 3 0.9997564
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6318720
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.7977273
## Max.cor.Y.rpart 0.5 0.7788018
## Max.cor.Y.glm 0.4 0.7774086
## Interact.High.cor.Y.glm 0.5 0.7795824
## Low.cor.X.glm 0.5 0.8009153
## All.X.glm 0.5 0.8045714
## All.X.bayesglm 0.5 0.8064147
## All.X.no.rnorm.rpart 0.5 0.7788018
## All.X.no.rnorm.rf 0.5 0.9933921
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5381485 0.0000000 0.5000000
## Random.myrandom_classfr 0.4618515 0.0000000 0.4820959
## Max.cor.Y.cv.0.rpart 0.5381485 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8189217 0.6341232 0.8158110
## Max.cor.Y.rpart 0.7874230 0.5681744 0.7975651
## Max.cor.Y.glm 0.8057097 0.6067760 0.8257233
## Interact.High.cor.Y.glm 0.8026640 0.6002968 0.8211155
## Low.cor.X.glm 0.7874230 0.5705824 0.8216216
## All.X.glm 0.7802875 0.5567983 0.8306911
## All.X.bayesglm 0.7833456 0.5628973 0.8273409
## All.X.no.rnorm.rpart 0.7925199 0.5770549 0.7975651
## All.X.no.rnorm.rf 0.7782549 0.5516744 0.8374984
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6323988
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.7380645
## Max.cor.Y.rpart 0.5 0.7352185
## Max.cor.Y.glm 0.4 0.7422935
## Interact.High.cor.Y.glm 0.4 0.7432099
## Low.cor.X.glm 0.6 0.7358739
## All.X.glm 0.4 0.7432591
## All.X.bayesglm 0.6 0.7378129
## All.X.no.rnorm.rpart 0.5 0.7352185
## All.X.no.rnorm.rf 0.4 0.7469586
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5375854 0.0000000
## Random.myrandom_classfr 0.4624146 0.0000000
## Max.cor.Y.cv.0.rpart 0.5375854 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7687927 0.5319756
## Max.cor.Y.rpart 0.7653759 0.5253058
## Max.cor.Y.glm 0.7619590 0.5211301
## Interact.High.cor.Y.glm 0.7630979 0.5233392
## Low.cor.X.glm 0.7710706 0.5354595
## All.X.glm 0.7505695 0.5018214
## All.X.bayesglm 0.7733485 0.5399220
## All.X.no.rnorm.rpart 0.7653759 0.5253058
## All.X.no.rnorm.rf 0.7630979 0.5243225
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.myMFO_classfr 2.67379679 250.0000000
## Random.myrandom_classfr 4.09836066 500.0000000
## Max.cor.Y.cv.0.rpart 0.90579710 90.9090909
## Max.cor.Y.cv.0.cp.0.rpart 2.16919740 125.0000000
## Max.cor.Y.rpart 0.96153846 76.9230769
## Max.cor.Y.glm 1.07411386 76.9230769
## Interact.High.cor.Y.glm 1.06044539 62.5000000
## Low.cor.X.glm 0.74349442 7.6923077
## All.X.glm 0.68399453 5.1282051
## All.X.bayesglm 0.44464206 3.4602076
## All.X.no.rnorm.rpart 0.67750678 13.8888889
## All.X.no.rnorm.rf 0.06582412 0.2104377
## inv.aic.fit
## MFO.myMFO_classfr NA
## Random.myrandom_classfr NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.glm 0.001079885
## Interact.High.cor.Y.glm 0.001077443
## Low.cor.X.glm 0.001085097
## All.X.glm 0.001066220
## All.X.bayesglm 0.001058667
## All.X.no.rnorm.rpart NA
## All.X.no.rnorm.rf NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.## Warning: Removed 4 rows containing missing values (geom_path).## Warning: Removed 87 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 10 All.X.bayesglm 0.7733485 0.8273409 0.5399220
## 8 Low.cor.X.glm 0.7710706 0.8216216 0.5354595
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7687927 0.8158110 0.5319756
## 5 Max.cor.Y.rpart 0.7653759 0.7975651 0.5253058
## 11 All.X.no.rnorm.rpart 0.7653759 0.7975651 0.5253058
## 12 All.X.no.rnorm.rf 0.7630979 0.8374984 0.5243225
## 7 Interact.High.cor.Y.glm 0.7630979 0.8211155 0.5233392
## 6 Max.cor.Y.glm 0.7619590 0.8257233 0.5211301
## 9 All.X.glm 0.7505695 0.8306911 0.5018214
## 1 MFO.myMFO_classfr 0.5375854 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5375854 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.4624146 0.4820959 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 10 944.5838 0.6
## 8 921.5765 0.6
## 4 NA 0.4
## 5 NA 0.5
## 11 NA 0.5
## 12 NA 0.4
## 7 928.1232 0.4
## 6 926.0247 0.4
## 9 937.8927 0.4
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.## Warning: Removed 38 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.X.bayesglm"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4303 -0.6534 -0.1522 0.5605 3.4677
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 0.937071 2.091960 0.448
## biddable 1.703716 0.204424 8.334
## D.ratio.nstopwrds.nwrds 0.175284 2.059335 0.085
## D.npnct15.log 0.879466 0.886235 0.992
## D.T.screen 0.172184 0.680012 0.253
## D.npnct03.log 2.522462 1.895013 1.331
## D.T.used -0.744717 0.515315 -1.445
## D.T.excellent 0.016522 0.416915 0.040
## D.T.great -0.425162 0.563190 -0.755
## D.ratio.sum.TfIdf.nwrds 0.162914 0.509521 0.320
## D.T.good -0.285617 0.653384 -0.437
## D.npnct01.log 0.198426 0.676975 0.293
## D.T.veri -0.760489 0.579365 -1.313
## D.T.work -0.044486 0.703734 -0.063
## D.T.scratch -0.293226 0.819011 -0.358
## D.T.use 1.712065 0.843921 2.029
## D.T.condition 0.639269 0.756760 0.845
## D.npnct11.log 0.243735 0.374585 0.651
## .rnorm -0.089355 0.094853 -0.942
## D.T.ipad -1.519099 0.913835 -1.662
## D.npnct10.log 1.506435 1.750646 0.861
## D.nstopwrds.log 0.270510 0.526382 0.514
## D.sum.TfIdf 0.287710 0.164996 1.744
## D.T.new 0.482312 1.074518 0.449
## D.npnct13.log -0.238872 0.364117 -0.656
## D.npnct08.log -0.191340 0.712565 -0.269
## D.T.condit -0.031605 0.567855 -0.056
## color.fctrBlack 0.130805 0.248007 0.527
## color.fctrGold 0.089119 0.542317 0.164
## `color.fctrSpace Gray` 0.247886 0.351594 0.705
## color.fctrWhite -0.192728 0.248780 -0.775
## storage.fctr128 1.307923 0.565527 2.313
## storage.fctr32 -0.031948 0.261857 -0.122
## storage.fctr64 0.145673 0.283170 0.514
## storage.fctrUnknown 0.951992 0.485334 1.962
## D.npnct16.log 0.832696 2.077839 0.401
## D.npnct24.log 0.152843 2.493225 0.061
## D.T.this 0.822634 1.002052 0.821
## D.npnct06.log -3.253666 2.433477 -1.337
## D.T.box -1.755432 0.898280 -1.954
## D.npnct28.log -1.398481 9.118640 -0.153
## D.T.like -1.940256 1.079835 -1.797
## D.npnct12.log 0.279952 0.677773 0.413
## D.nchrs.log -0.216655 0.476760 -0.454
## D.nwrds.log -0.080621 0.698117 -0.115
## D.npnct09.log -1.142154 5.967120 -0.191
## D.nwrds.unq.log -0.289712 0.618633 -0.468
## D.ndgts.log -0.018167 0.419035 -0.043
## `carrier.fctrAT&T` 0.173813 0.722168 0.241
## carrier.fctrOther 1.615166 1.837332 0.879
## carrier.fctrSprint 0.617647 0.982417 0.629
## `carrier.fctrT-Mobile` -0.075141 1.006689 -0.075
## carrier.fctrUnknown 0.154089 0.701345 0.220
## carrier.fctrVerizon 0.176961 0.749910 0.236
## D.nuppr.log -0.208839 0.249578 -0.837
## cellular.fctr1 -0.369343 0.685362 -0.539
## cellular.fctrUnknown -1.008821 0.765605 -1.318
## D.npnct14.log -1.453862 0.895492 -1.624
## `productline.fctriPad 1` -0.412108 0.337113 -1.222
## `productline.fctriPad 3` 0.641517 0.374815 1.712
## `productline.fctriPad 4` 0.511000 0.407442 1.254
## `productline.fctriPad 5` 0.581777 1.967610 0.296
## `productline.fctriPad Air` 0.937362 0.438988 2.135
## `productline.fctriPad Air 2` 2.246081 0.539658 4.162
## `productline.fctriPad mini` -0.149427 0.327597 -0.456
## `productline.fctriPad mini 2` 1.287360 0.472938 2.722
## `productline.fctriPad mini 3` 1.513268 0.542259 2.791
## `productline.fctriPad mini Retina` 0.398660 1.280702 0.311
## productline.fctrUnknown -0.612096 0.428579 -1.428
## D.npnct05.log -1.826474 1.317425 -1.386
## `condition.fctrFor parts or not working` -0.886337 0.342779 -2.586
## `condition.fctrManufacturer refurbished` 0.511012 0.743615 0.687
## condition.fctrNew 0.162843 0.332660 0.490
## `condition.fctrNew other (see details)` 0.607996 0.457064 1.330
## `condition.fctrSeller refurbished` -0.848881 0.417718 -2.032
## startprice -0.011286 0.001284 -8.793
## Pr(>|z|)
## (Intercept) 0.65420
## biddable < 2e-16 ***
## D.ratio.nstopwrds.nwrds 0.93217
## D.npnct15.log 0.32102
## D.T.screen 0.80011
## D.npnct03.log 0.18315
## D.T.used 0.14841
## D.T.excellent 0.96839
## D.T.great 0.45030
## D.ratio.sum.TfIdf.nwrds 0.74917
## D.T.good 0.66201
## D.npnct01.log 0.76944
## D.T.veri 0.18931
## D.T.work 0.94960
## D.T.scratch 0.72032
## D.T.use 0.04249 *
## D.T.condition 0.39825
## D.npnct11.log 0.51525
## .rnorm 0.34617
## D.T.ipad 0.09645 .
## D.npnct10.log 0.38951
## D.nstopwrds.log 0.60732
## D.sum.TfIdf 0.08120 .
## D.T.new 0.65353
## D.npnct13.log 0.51180
## D.npnct08.log 0.78830
## D.T.condit 0.95562
## color.fctrBlack 0.59790
## color.fctrGold 0.86947
## `color.fctrSpace Gray` 0.48079
## color.fctrWhite 0.43852
## storage.fctr128 0.02074 *
## storage.fctr32 0.90289
## storage.fctr64 0.60695
## storage.fctrUnknown 0.04982 *
## D.npnct16.log 0.68860
## D.npnct24.log 0.95112
## D.T.this 0.41168
## D.npnct06.log 0.18121
## D.T.box 0.05068 .
## D.npnct28.log 0.87811
## D.T.like 0.07237 .
## D.npnct12.log 0.67957
## D.nchrs.log 0.64952
## D.nwrds.log 0.90806
## D.npnct09.log 0.84821
## D.nwrds.unq.log 0.63956
## D.ndgts.log 0.96542
## `carrier.fctrAT&T` 0.80980
## carrier.fctrOther 0.37936
## carrier.fctrSprint 0.52954
## `carrier.fctrT-Mobile` 0.94050
## carrier.fctrUnknown 0.82610
## carrier.fctrVerizon 0.81345
## D.nuppr.log 0.40272
## cellular.fctr1 0.58995
## cellular.fctrUnknown 0.18761
## D.npnct14.log 0.10448
## `productline.fctriPad 1` 0.22153
## `productline.fctriPad 3` 0.08698 .
## `productline.fctriPad 4` 0.20978
## `productline.fctriPad 5` 0.76748
## `productline.fctriPad Air` 0.03274 *
## `productline.fctriPad Air 2` 3.15e-05 ***
## `productline.fctriPad mini` 0.64830
## `productline.fctriPad mini 2` 0.00649 **
## `productline.fctriPad mini 3` 0.00526 **
## `productline.fctriPad mini Retina` 0.75559
## productline.fctrUnknown 0.15323
## D.npnct05.log 0.16563
## `condition.fctrFor parts or not working` 0.00972 **
## `condition.fctrManufacturer refurbished` 0.49196
## condition.fctrNew 0.62448
## `condition.fctrNew other (see details)` 0.18345
## `condition.fctrSeller refurbished` 0.04213 *
## startprice < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1357.00 on 982 degrees of freedom
## Residual deviance: 792.58 on 907 degrees of freedom
## AIC: 944.58
##
## Number of Fisher Scoring iterations: 15## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## N importance All.X.bayesglm.importance
## startprice 100.000000 100.000000 100.000000
## biddable 94.310602 94.310602 94.310602
## D.ratio.nstopwrds.nwrds 31.889829 31.889829 31.889829
## storage.fctr 29.860074 29.860074 29.860074
## .rnorm 23.254245 23.254245 23.254245
## D.npnct03.log 20.419225 20.419225 20.419225
## D.npnct15.log 20.275980 20.275980 20.275980
## D.T.use 19.691942 19.691942 19.691942
## D.T.excellent 19.434213 19.434213 19.434213
## D.npnct01.log 19.087993 19.087993 19.087993
## D.T.great 18.758918 18.758918 18.758918
## D.npnct10.log 18.523312 18.523312 18.523312
## D.npnct28.log 18.231293 18.231293 18.231293
## D.T.screen 18.072562 18.072562 18.072562
## D.T.veri 18.035507 18.035507 18.035507
## D.npnct09.log 17.729108 17.729108 17.729108
## D.T.work 17.468060 17.468060 17.468060
## D.T.used 17.464742 17.464742 17.464742
## D.T.good 17.160002 17.160002 17.160002
## D.T.new 16.952602 16.952602 16.952602
## D.T.box 16.334827 16.334827 16.334827
## D.T.scratch 16.074885 16.074885 16.074885
## D.T.like 15.956529 15.956529 15.956529
## D.ratio.sum.TfIdf.nwrds 15.904541 15.904541 15.904541
## D.npnct12.log 15.809966 15.809966 15.809966
## D.npnct08.log 15.782866 15.782866 15.782866
## D.T.ipad 15.017975 15.017975 15.017975
## D.T.this 14.985344 14.985344 14.985344
## D.npnct16.log 14.487031 14.487031 14.487031
## D.npnct06.log 14.453294 14.453294 14.453294
## D.T.condition 14.428406 14.428406 14.428406
## D.npnct14.log 13.696145 13.696145 13.696145
## D.sum.TfIdf 13.551795 13.551795 13.551795
## D.npnct11.log 13.283004 13.283004 13.283004
## color.fctr 13.264753 13.264753 13.264753
## D.npnct05.log 12.831702 12.831702 12.831702
## D.nstopwrds.log 12.426857 12.426857 12.426857
## D.ndgts.log 11.707870 11.707870 11.707870
## D.npnct13.log 11.662519 11.662519 11.662519
## D.npnct24.log 10.634368 10.634368 10.634368
## D.T.condit 9.083015 9.083015 9.083015
## D.nwrds.log 8.032188 8.032188 8.032188
## D.nuppr.log 6.029534 6.029534 6.029534
## D.nwrds.unq.log 5.328798 5.328798 5.328798
## D.nchrs.log 5.141309 5.141309 5.141309
## carrier.fctr 4.556164 4.556164 4.556164
## cellular.fctr 4.099331 4.099331 4.099331
## productline.fctr 1.204026 1.204026 1.204026
## condition.fctr 0.000000 0.000000 0.000000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 49
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.All.X.bayesglm.prob
## 45 10045 Y 0.245438810
## 141 10141 Y 0.290677468
## 30 10030 Y 0.329411585
## 76 10076 Y 0.356331282
## 154 10154 Y 0.380345671
## 32 10032 Y 0.532398646
## 108 10108 Y 0.552290875
## 48 10048 Y 0.557047361
## 31 10031 Y 0.573073578
## 50 10050 Y 0.586466065
## 2 10002 Y 0.945547042
## 4 10004 N 0.594013810
## 281 10281 Y 0.999314697
## 1283 11283 N 0.001148433
## 127 10127 N 0.788803520
## 103 10103 N 0.951678792
## sold.fctr.predict.All.X.bayesglm
## 45 N
## 141 N
## 30 N
## 76 N
## 154 N
## 32 N
## 108 N
## 48 N
## 31 N
## 50 N
## 2 Y
## 4 N
## 281 Y
## 1283 N
## 127 Y
## 103 Y
## sold.fctr.predict.All.X.bayesglm.accurate
## 45 FALSE
## 141 FALSE
## 30 FALSE
## 76 FALSE
## 154 FALSE
## 32 FALSE
## 108 FALSE
## 48 FALSE
## 31 FALSE
## 50 FALSE
## 2 TRUE
## 4 TRUE
## 281 TRUE
## 1283 TRUE
## 127 FALSE
## 103 FALSE
## sold.fctr.predict.All.X.bayesglm.error .label
## 45 -0.35456119 10045
## 141 -0.30932253 10141
## 30 -0.27058842 10030
## 76 -0.24366872 10076
## 154 -0.21965433 10154
## 32 -0.06760135 10032
## 108 -0.04770913 10108
## 48 -0.04295264 10048
## 31 -0.02692642 10031
## 50 -0.01353393 10050
## 2 0.00000000 10002
## 4 0.00000000 10004
## 281 0.00000000 10281
## 1283 0.00000000 11283
## 127 0.18880352 10127
## 103 0.35167879 10103
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.All.X.bayesglm.prob
## 1705 11705 Y 0.0008634853
## 1804 11804 Y 0.0025787635
## 353 10353 Y 0.0099759203
## 1514 11514 Y 0.0110009072
## 935 10935 Y 0.0131443959
## 1776 11776 Y 0.0172380682
## sold.fctr.predict.All.X.bayesglm
## 1705 N
## 1804 N
## 353 N
## 1514 N
## 935 N
## 1776 N
## sold.fctr.predict.All.X.bayesglm.accurate
## 1705 FALSE
## 1804 FALSE
## 353 FALSE
## 1514 FALSE
## 935 FALSE
## 1776 FALSE
## sold.fctr.predict.All.X.bayesglm.error
## 1705 -0.5991365
## 1804 -0.5974212
## 353 -0.5900241
## 1514 -0.5889991
## 935 -0.5868556
## 1776 -0.5827619
## UniqueID sold.fctr sold.fctr.predict.All.X.bayesglm.prob
## 353 10353 Y 0.00997592
## 1524 11524 Y 0.03103558
## 277 10277 Y 0.24659270
## 1816 11816 Y 0.24802498
## 1725 11725 Y 0.51252385
## 785 10785 N 0.71880002
## sold.fctr.predict.All.X.bayesglm
## 353 N
## 1524 N
## 277 N
## 1816 N
## 1725 N
## 785 Y
## sold.fctr.predict.All.X.bayesglm.accurate
## 353 FALSE
## 1524 FALSE
## 277 FALSE
## 1816 FALSE
## 1725 FALSE
## 785 FALSE
## sold.fctr.predict.All.X.bayesglm.error
## 353 -0.59002408
## 1524 -0.56896442
## 277 -0.35340730
## 1816 -0.35197502
## 1725 -0.08747615
## 785 0.11880002
## UniqueID sold.fctr sold.fctr.predict.All.X.bayesglm.prob
## 1391 11391 N 0.9626562
## 1506 11506 N 0.9640943
## 283 10283 N 0.9723933
## 1250 11250 N 0.9724371
## 491 10491 N 0.9755839
## 1207 11207 N 0.9959046
## sold.fctr.predict.All.X.bayesglm
## 1391 Y
## 1506 Y
## 283 Y
## 1250 Y
## 491 Y
## 1207 Y
## sold.fctr.predict.All.X.bayesglm.accurate
## 1391 FALSE
## 1506 FALSE
## 283 FALSE
## 1250 FALSE
## 491 FALSE
## 1207 FALSE
## sold.fctr.predict.All.X.bayesglm.error
## 1391 0.3626562
## 1506 0.3640943
## 283 0.3723933
## 1250 0.3724371
## 491 0.3755839
## 1207 0.3959046
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 99.433 114.515 15.082
## 13 fit.models 7 3 114.515 NA NAprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "D.npnct02.log" "D.npnct04.log" "D.npnct07.log" "D.npnct17.log"
## [5] "D.npnct18.log" "D.npnct19.log" "D.npnct20.log" "D.npnct21.log"
## [9] "D.npnct22.log" "D.npnct23.log" "D.npnct25.log" "D.npnct26.log"
## [13] "D.npnct27.log" "D.npnct29.log" "D.npnct30.log" "D.P.http"print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## [1] "D.npnct02.log" "D.npnct04.log" "D.npnct07.log" "D.npnct17.log"
## [5] "D.npnct18.log" "D.npnct19.log" "D.npnct20.log" "D.npnct21.log"
## [9] "D.npnct22.log" "D.npnct23.log" "D.npnct25.log" "D.npnct26.log"
## [13] "D.npnct27.log" "D.npnct29.log" "D.npnct30.log" "D.P.http"print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "D.npnct02.log"
## [2] "D.npnct04.log"
## [3] "D.npnct07.log"
## [4] "D.npnct17.log"
## [5] "D.npnct18.log"
## [6] "D.npnct19.log"
## [7] "D.npnct20.log"
## [8] "D.npnct21.log"
## [9] "D.npnct22.log"
## [10] "D.npnct23.log"
## [11] "D.npnct25.log"
## [12] "D.npnct26.log"
## [13] "D.npnct27.log"
## [14] "D.npnct29.log"
## [15] "D.npnct30.log"
## [16] "D.P.http"
## [17] "sold.fctr.predict.All.X.bayesglm.prob"
## [18] "sold.fctr.predict.All.X.bayesglm"
## [19] "sold.fctr.predict.All.X.bayesglm.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 114.515 118.983 4.468
## 14 fit.data.training 8 0 118.984 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:randomForest':
##
## combine
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.bayesglm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6284 -0.6957 -0.2270 0.6309 3.5644
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 2.4940478 1.8205743 1.370
## biddable 1.5002243 0.1410784 10.634
## D.ratio.nstopwrds.nwrds -1.5848593 1.8081451 -0.877
## D.npnct15.log 1.5346369 0.6739445 2.277
## D.T.screen 0.2674762 0.4906759 0.545
## D.npnct03.log -0.0616806 1.0232171 -0.060
## D.T.used 0.2472134 0.4666479 0.530
## D.T.excellent 0.4724161 0.2613297 1.808
## D.T.great 0.1050616 0.4254513 0.247
## D.ratio.sum.TfIdf.nwrds -0.3124592 0.2126950 -1.469
## D.T.good 0.3702132 0.4710095 0.786
## D.npnct01.log 0.2501083 0.4511958 0.554
## D.T.veri -0.1267358 0.4580779 -0.277
## D.T.work -0.0577937 0.4767399 -0.121
## D.T.scratch 0.4077704 0.5306733 0.768
## D.T.use 0.6829176 0.5017613 1.361
## D.T.condition 0.5592848 0.5108873 1.095
## D.npnct11.log 0.0428792 0.2372255 0.181
## .rnorm -0.0450299 0.0638795 -0.705
## D.T.ipad -1.0530755 0.6144613 -1.714
## D.npnct10.log 0.7366927 1.2807226 0.575
## D.nstopwrds.log 0.4941682 0.4502497 1.098
## D.sum.TfIdf 0.2591856 0.1029702 2.517
## D.T.new 0.9903944 0.5935956 1.668
## D.npnct13.log -0.1206703 0.2346817 -0.514
## D.npnct08.log -0.2091592 0.4740681 -0.441
## D.T.condit -0.1839920 0.3449782 -0.533
## color.fctrBlack 0.0982886 0.1735319 0.566
## color.fctrGold -0.2662674 0.4296062 -0.620
## `color.fctrSpace Gray` 0.0838630 0.2322299 0.361
## color.fctrWhite -0.0618222 0.1726691 -0.358
## storage.fctr128 1.1638132 0.4174780 2.788
## storage.fctr32 0.0580894 0.1774284 0.327
## storage.fctr64 0.5820777 0.1952816 2.981
## storage.fctrUnknown 0.4563097 0.3453078 1.321
## D.npnct16.log 1.0194711 1.1663121 0.874
## D.npnct24.log -0.4275428 1.9481032 -0.219
## D.T.this 0.5806955 0.6925216 0.839
## D.npnct06.log -1.9406436 1.3245304 -1.465
## D.T.box -0.9385091 0.5718171 -1.641
## D.npnct28.log -1.3025514 9.5740239 -0.136
## D.T.like -1.1228962 0.5077133 -2.212
## D.npnct12.log -0.0154253 0.4329299 -0.036
## D.nchrs.log -0.2432793 0.4323331 -0.563
## D.nwrds.log 0.1048407 0.5853258 0.179
## D.npnct09.log -2.0371705 5.6480035 -0.361
## D.nwrds.unq.log -0.9610370 0.4168534 -2.305
## D.ndgts.log 0.4279401 0.2794525 1.531
## `carrier.fctrAT&T` 0.4092665 0.6590391 0.621
## carrier.fctrOther 1.4503492 1.7929639 0.809
## carrier.fctrSprint 1.0126743 0.7785575 1.301
## `carrier.fctrT-Mobile` 0.2827132 0.8891760 0.318
## carrier.fctrUnknown 0.3801678 0.6429691 0.591
## carrier.fctrVerizon 0.5886241 0.6688491 0.880
## D.nuppr.log -0.1867722 0.1659942 -1.125
## cellular.fctr1 -0.2448980 0.6411069 -0.382
## cellular.fctrUnknown -0.9271995 0.6822509 -1.359
## D.npnct14.log -0.9708152 0.5439533 -1.785
## `productline.fctriPad 1` -0.5760408 0.2356176 -2.445
## `productline.fctriPad 3` 0.4141947 0.2621043 1.580
## `productline.fctriPad 4` 0.5590553 0.2793994 2.001
## `productline.fctriPad 5` 0.9207126 1.9217758 0.479
## `productline.fctriPad Air` 1.4013545 0.3042178 4.606
## `productline.fctriPad Air 2` 2.7587104 0.3895761 7.081
## `productline.fctriPad mini` 0.0178340 0.2246274 0.079
## `productline.fctriPad mini 2` 1.0130433 0.3181247 3.184
## `productline.fctriPad mini 3` 1.4559063 0.4181033 3.482
## `productline.fctriPad mini Retina` 2.2416697 0.8249341 2.717
## productline.fctrUnknown -0.1150628 0.3104546 -0.371
## D.npnct05.log -2.3759048 0.9940217 -2.390
## `condition.fctrFor parts or not working` -0.6648786 0.2252875 -2.951
## `condition.fctrManufacturer refurbished` 0.4972006 0.4566971 1.089
## condition.fctrNew 0.2341055 0.2332224 1.004
## `condition.fctrNew other (see details)` 0.3451259 0.3519764 0.981
## `condition.fctrSeller refurbished` -0.3771323 0.2907394 -1.297
## startprice -0.0114657 0.0009169 -12.505
## Pr(>|z|)
## (Intercept) 0.170711
## biddable < 2e-16 ***
## D.ratio.nstopwrds.nwrds 0.380752
## D.npnct15.log 0.022780 *
## D.T.screen 0.585672
## D.npnct03.log 0.951932
## D.T.used 0.596275
## D.T.excellent 0.070647 .
## D.T.great 0.804954
## D.ratio.sum.TfIdf.nwrds 0.141820
## D.T.good 0.431868
## D.npnct01.log 0.579358
## D.T.veri 0.782035
## D.T.work 0.903511
## D.T.scratch 0.442248
## D.T.use 0.173501
## D.T.condition 0.273634
## D.npnct11.log 0.856561
## .rnorm 0.480861
## D.T.ipad 0.086562 .
## D.npnct10.log 0.565145
## D.nstopwrds.log 0.272404
## D.sum.TfIdf 0.011833 *
## D.T.new 0.095223 .
## D.npnct13.log 0.607121
## D.npnct08.log 0.659068
## D.T.condit 0.593795
## color.fctrBlack 0.571121
## color.fctrGold 0.535393
## `color.fctrSpace Gray` 0.718009
## color.fctrWhite 0.720314
## storage.fctr128 0.005308 **
## storage.fctr32 0.743368
## storage.fctr64 0.002876 **
## storage.fctrUnknown 0.186349
## D.npnct16.log 0.382065
## D.npnct24.log 0.826287
## D.T.this 0.401737
## D.npnct06.log 0.142878
## D.T.box 0.100740
## D.npnct28.log 0.891781
## D.T.like 0.026989 *
## D.npnct12.log 0.971577
## D.nchrs.log 0.573631
## D.nwrds.log 0.857847
## D.npnct09.log 0.718332
## D.nwrds.unq.log 0.021141 *
## D.ndgts.log 0.125682
## `carrier.fctrAT&T` 0.534596
## carrier.fctrOther 0.418566
## carrier.fctrSprint 0.193359
## `carrier.fctrT-Mobile` 0.750523
## carrier.fctrUnknown 0.554340
## carrier.fctrVerizon 0.378829
## D.nuppr.log 0.260516
## cellular.fctr1 0.702467
## cellular.fctrUnknown 0.174137
## D.npnct14.log 0.074304 .
## `productline.fctriPad 1` 0.014493 *
## `productline.fctriPad 3` 0.114046
## `productline.fctriPad 4` 0.045401 *
## `productline.fctriPad 5` 0.631871
## `productline.fctriPad Air` 4.10e-06 ***
## `productline.fctriPad Air 2` 1.43e-12 ***
## `productline.fctriPad mini` 0.936719
## `productline.fctriPad mini 2` 0.001450 **
## `productline.fctriPad mini 3` 0.000497 ***
## `productline.fctriPad mini Retina` 0.006580 **
## productline.fctrUnknown 0.710915
## D.npnct05.log 0.016839 *
## `condition.fctrFor parts or not working` 0.003165 **
## `condition.fctrManufacturer refurbished` 0.276292
## condition.fctrNew 0.315482
## `condition.fctrNew other (see details)` 0.326821
## `condition.fctrSeller refurbished` 0.194580
## startprice < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2569.2 on 1860 degrees of freedom
## Residual deviance: 1629.2 on 1785 degrees of freedom
## AIC: 1781.2
##
## Number of Fisher Scoring iterations: 14
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6321205
## 2 0.1 0.6877828
## 3 0.2 0.7404651
## 4 0.3 0.7636933
## 5 0.4 0.7810718
## 6 0.5 0.7842200
## 7 0.6 0.7854454
## 8 0.7 0.7588986
## 9 0.8 0.6570997
## 10 0.9 0.4014733
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Final.bayesglm.N
## 1 N 893
## 2 Y 234
## sold.fctr.predict.Final.bayesglm.Y
## 1 108
## 2 626
## Prediction
## Reference N Y
## N 893 108
## Y 234 626
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.162278e-01 6.264794e-01 7.978694e-01 8.335837e-01 5.378829e-01
## AccuracyPValue McnemarPValue
## 2.209083e-140 1.387310e-11## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.bayesglm bayesglm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.T.use, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.nstopwrds.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, storage.fctr, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, D.nuppr.log, cellular.fctr, D.npnct14.log, productline.fctr, D.npnct05.log, condition.fctr, startprice
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.255 0.443
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8769231 0.6 0.7854454 0.7893677
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit min.aic.fit
## 1 0.7978694 0.8335837 0.5747625 1781.249
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02459266 0.0490822rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 118.984 126.206 7.222
## 15 fit.data.training 8 1 126.206 NA NA#```
#```{r fit.data.training_1, cache=FALSE}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.6sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## N.x All.X.bayesglm.importance N.y
## startprice 100.000000 100.000000 100.000000
## biddable 94.310602 94.310602 95.299950
## D.ratio.nstopwrds.nwrds 31.889829 31.889829 27.859787
## storage.fctr 29.860074 29.860074 26.541908
## .rnorm 23.254245 23.254245 22.114723
## D.npnct15.log 20.275980 20.275980 20.305331
## D.T.screen 18.072562 18.072562 19.726099
## D.npnct03.log 20.419225 20.419225 19.665570
## D.T.used 17.464742 17.464742 19.516675
## D.npnct01.log 19.087993 19.087993 19.391086
## D.T.veri 18.035507 18.035507 19.326997
## D.T.excellent 19.434213 19.434213 19.194772
## D.T.great 18.758918 18.758918 18.804571
## D.npnct10.log 18.523312 18.523312 18.664578
## D.T.use 19.691942 19.691942 18.295740
## D.npnct28.log 18.231293 18.231293 18.201710
## D.T.scratch 16.074885 16.074885 18.006367
## D.npnct09.log 17.729108 17.729108 17.784158
## D.T.good 17.160002 17.160002 17.750657
## D.npnct11.log 13.283004 13.283004 17.371947
## D.T.work 17.468060 17.468060 17.034992
## D.npnct08.log 15.782866 15.782866 16.632329
## D.ratio.sum.TfIdf.nwrds 15.904541 15.904541 16.430027
## D.T.like 15.956529 15.956529 16.404780
## D.npnct12.log 15.809966 15.809966 16.233551
## D.T.ipad 15.017975 15.017975 16.197784
## D.T.box 16.334827 16.334827 16.090483
## D.npnct16.log 14.487031 14.487031 16.049213
## D.T.this 14.985344 14.985344 15.843350
## D.npnct06.log 14.453294 14.453294 15.820207
## D.T.new 16.952602 16.952602 15.800947
## D.T.condition 14.428406 14.428406 15.689924
## D.sum.TfIdf 13.551795 13.551795 15.392458
## D.npnct14.log 13.696145 13.696145 15.315098
## D.nstopwrds.log 12.426857 12.426857 15.264280
## D.ndgts.log 11.707870 11.707870 14.653974
## D.npnct05.log 12.831702 12.831702 13.978284
## D.npnct13.log 11.662519 11.662519 13.686969
## color.fctr 13.264753 13.264753 13.442587
## D.npnct24.log 10.634368 10.634368 12.690023
## D.T.condit 9.083015 9.083015 11.418916
## D.nwrds.log 8.032188 8.032188 11.099441
## D.nchrs.log 5.141309 5.141309 9.416932
## carrier.fctr 4.556164 4.556164 9.251206
## D.nwrds.unq.log 5.328798 5.328798 9.229843
## D.nuppr.log 6.029534 6.029534 9.062337
## cellular.fctr 4.099331 4.099331 8.141456
## productline.fctr 1.204026 1.204026 4.897012
## condition.fctr 0.000000 0.000000 0.000000
## importance Final.bayesglm.importance
## startprice 100.000000 100.000000
## biddable 95.299950 95.299950
## D.ratio.nstopwrds.nwrds 27.859787 27.859787
## storage.fctr 26.541908 26.541908
## .rnorm 22.114723 22.114723
## D.npnct15.log 20.305331 20.305331
## D.T.screen 19.726099 19.726099
## D.npnct03.log 19.665570 19.665570
## D.T.used 19.516675 19.516675
## D.npnct01.log 19.391086 19.391086
## D.T.veri 19.326997 19.326997
## D.T.excellent 19.194772 19.194772
## D.T.great 18.804571 18.804571
## D.npnct10.log 18.664578 18.664578
## D.T.use 18.295740 18.295740
## D.npnct28.log 18.201710 18.201710
## D.T.scratch 18.006367 18.006367
## D.npnct09.log 17.784158 17.784158
## D.T.good 17.750657 17.750657
## D.npnct11.log 17.371947 17.371947
## D.T.work 17.034992 17.034992
## D.npnct08.log 16.632329 16.632329
## D.ratio.sum.TfIdf.nwrds 16.430027 16.430027
## D.T.like 16.404780 16.404780
## D.npnct12.log 16.233551 16.233551
## D.T.ipad 16.197784 16.197784
## D.T.box 16.090483 16.090483
## D.npnct16.log 16.049213 16.049213
## D.T.this 15.843350 15.843350
## D.npnct06.log 15.820207 15.820207
## D.T.new 15.800947 15.800947
## D.T.condition 15.689924 15.689924
## D.sum.TfIdf 15.392458 15.392458
## D.npnct14.log 15.315098 15.315098
## D.nstopwrds.log 15.264280 15.264280
## D.ndgts.log 14.653974 14.653974
## D.npnct05.log 13.978284 13.978284
## D.npnct13.log 13.686969 13.686969
## color.fctr 13.442587 13.442587
## D.npnct24.log 12.690023 12.690023
## D.T.condit 11.418916 11.418916
## D.nwrds.log 11.099441 11.099441
## D.nchrs.log 9.416932 9.416932
## carrier.fctr 9.251206 9.251206
## D.nwrds.unq.log 9.229843 9.229843
## D.nuppr.log 9.062337 9.062337
## cellular.fctr 8.141456 8.141456
## productline.fctr 4.897012 4.897012
## condition.fctr 0.000000 0.000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 49
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 17 10017 Y 0.157733456
## 3 10003 Y 0.286755617
## 15 10015 Y 0.310533330
## 19 10019 Y 0.333259736
## 11 10011 Y 0.342809187
## 14 10014 Y 0.399491216
## 142 10142 Y 0.525554371
## 1 10001 N 0.461107267
## 2 10002 Y 0.941753141
## 91 10091 Y 0.884615627
## 1397 11397 N 0.000028024
## 193 10193 N 0.605597582
## 127 10127 N 0.669988474
## 182 10182 N 0.816353250
## 120 10120 N 0.844882566
## 103 10103 N 0.848454734
## sold.fctr.predict.Final.bayesglm
## 17 N
## 3 N
## 15 N
## 19 N
## 11 N
## 14 N
## 142 N
## 1 N
## 2 Y
## 91 Y
## 1397 N
## 193 Y
## 127 Y
## 182 Y
## 120 Y
## 103 Y
## sold.fctr.predict.Final.bayesglm.accurate
## 17 FALSE
## 3 FALSE
## 15 FALSE
## 19 FALSE
## 11 FALSE
## 14 FALSE
## 142 FALSE
## 1 TRUE
## 2 TRUE
## 91 TRUE
## 1397 TRUE
## 193 FALSE
## 127 FALSE
## 182 FALSE
## 120 FALSE
## 103 FALSE
## sold.fctr.predict.Final.bayesglm.error .label
## 17 -0.442266544 10017
## 3 -0.313244383 10003
## 15 -0.289466670 10015
## 19 -0.266740264 10019
## 11 -0.257190813 10011
## 14 -0.200508784 10014
## 142 -0.074445629 10142
## 1 0.000000000 10001
## 2 0.000000000 10002
## 91 0.000000000 10091
## 1397 0.000000000 11397
## 193 0.005597582 10193
## 127 0.069988474 10127
## 182 0.216353250 10182
## 120 0.244882566 10120
## 103 0.248454734 10103
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 1705 11705 Y 0.001742264
## 1359 11359 Y 0.002698180
## 1804 11804 Y 0.008645081
## 1817 11817 Y 0.020301775
## 1524 11524 Y 0.028512197
## 986 10986 Y 0.035521724
## sold.fctr.predict.Final.bayesglm
## 1705 N
## 1359 N
## 1804 N
## 1817 N
## 1524 N
## 986 N
## sold.fctr.predict.Final.bayesglm.accurate
## 1705 FALSE
## 1359 FALSE
## 1804 FALSE
## 1817 FALSE
## 1524 FALSE
## 986 FALSE
## sold.fctr.predict.Final.bayesglm.error
## 1705 -0.5982577
## 1359 -0.5973018
## 1804 -0.5913549
## 1817 -0.5796982
## 1524 -0.5714878
## 986 -0.5644783
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 1119 11119 Y 0.1051530
## 1784 11784 Y 0.1684722
## 381 10381 Y 0.3182307
## 760 10760 N 0.6674994
## 1239 11239 N 0.7397586
## 955 10955 N 0.7798860
## sold.fctr.predict.Final.bayesglm
## 1119 N
## 1784 N
## 381 N
## 760 Y
## 1239 Y
## 955 Y
## sold.fctr.predict.Final.bayesglm.accurate
## 1119 FALSE
## 1784 FALSE
## 381 FALSE
## 760 FALSE
## 1239 FALSE
## 955 FALSE
## sold.fctr.predict.Final.bayesglm.error
## 1119 -0.49484695
## 1784 -0.43152777
## 381 -0.28176928
## 760 0.06749942
## 1239 0.13975859
## 955 0.17988605
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 491 10491 N 0.9358808
## 283 10283 N 0.9371680
## 1250 11250 N 0.9443534
## 1506 11506 N 0.9462618
## 1471 11471 N 0.9662380
## 594 10594 N 0.9683906
## sold.fctr.predict.Final.bayesglm
## 491 Y
## 283 Y
## 1250 Y
## 1506 Y
## 1471 Y
## 594 Y
## sold.fctr.predict.Final.bayesglm.accurate
## 491 FALSE
## 283 FALSE
## 1250 FALSE
## 1506 FALSE
## 1471 FALSE
## 594 FALSE
## sold.fctr.predict.Final.bayesglm.error
## 491 0.3358808
## 283 0.3371680
## 1250 0.3443534
## 1506 0.3462618
## 1471 0.3662380
## 594 0.3683906
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.Final.bayesglm.prob"
## [2] "sold.fctr.predict.Final.bayesglm"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 126.206 131.644 5.439
## 16 predict.data.new 9 0 131.645 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.6if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 49## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## 1862 11862 <NA> 1.438908e-01
## 1865 11865 <NA> 7.698790e-01
## 1891 11891 <NA> 9.585644e-01
## 2625 12625 <NA> 7.931161e-06
## sold.fctr.predict.Final.bayesglm
## 1862 N
## 1865 Y
## 1891 Y
## 2625 N
## sold.fctr.predict.Final.bayesglm.accurate
## 1862 NA
## 1865 NA
## 1891 NA
## 2625 NA
## sold.fctr.predict.Final.bayesglm.error .label
## 1862 0 11862
## 1865 0 11865
## 1891 0 11891
## 2625 0 12625
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## NA NA <NA> NA
## NA.1 NA <NA> NA
## NA.2 NA <NA> NA
## NA.3 NA <NA> NA
## NA.4 NA <NA> NA
## NA.5 NA <NA> NA
## sold.fctr.predict.Final.bayesglm
## NA <NA>
## NA.1 <NA>
## NA.2 <NA>
## NA.3 <NA>
## NA.4 <NA>
## NA.5 <NA>
## sold.fctr.predict.Final.bayesglm.accurate
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## sold.fctr.predict.Final.bayesglm.error
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## NA.3 NA <NA> NA
## NA.35 NA <NA> NA
## NA.160 NA <NA> NA
## NA.212 NA <NA> NA
## NA.593 NA <NA> NA
## NA.702 NA <NA> NA
## sold.fctr.predict.Final.bayesglm
## NA.3 <NA>
## NA.35 <NA>
## NA.160 <NA>
## NA.212 <NA>
## NA.593 <NA>
## NA.702 <NA>
## sold.fctr.predict.Final.bayesglm.accurate
## NA.3 NA
## NA.35 NA
## NA.160 NA
## NA.212 NA
## NA.593 NA
## NA.702 NA
## sold.fctr.predict.Final.bayesglm.error
## NA.3 NA
## NA.35 NA
## NA.160 NA
## NA.212 NA
## NA.593 NA
## NA.702 NA
## UniqueID sold.fctr sold.fctr.predict.Final.bayesglm.prob
## NA.792 NA <NA> NA
## NA.793 NA <NA> NA
## NA.794 NA <NA> NA
## NA.795 NA <NA> NA
## NA.796 NA <NA> NA
## NA.797 NA <NA> NA
## sold.fctr.predict.Final.bayesglm
## NA.792 <NA>
## NA.793 <NA>
## NA.794 <NA>
## NA.795 <NA>
## NA.796 <NA>
## NA.797 <NA>
## sold.fctr.predict.Final.bayesglm.accurate
## NA.792 NA
## NA.793 NA
## NA.794 NA
## NA.795 NA
## NA.796 NA
## NA.797 NA
## sold.fctr.predict.Final.bayesglm.error
## NA.792 NA
## NA.793 NA
## NA.794 NA
## NA.795 NA
## NA.796 NA
## NA.797 NA## Warning: Removed 798 rows containing missing values (geom_point).
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.6print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.X.bayesglm"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.bayesglm"print(dim(glb_fitobs_df))## [1] 983 76print(dsp_models_df)## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 10 All.X.bayesglm 0.7733485 0.8273409 0.5399220
## 8 Low.cor.X.glm 0.7710706 0.8216216 0.5354595
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7687927 0.8158110 0.5319756
## 5 Max.cor.Y.rpart 0.7653759 0.7975651 0.5253058
## 11 All.X.no.rnorm.rpart 0.7653759 0.7975651 0.5253058
## 12 All.X.no.rnorm.rf 0.7630979 0.8374984 0.5243225
## 7 Interact.High.cor.Y.glm 0.7630979 0.8211155 0.5233392
## 6 Max.cor.Y.glm 0.7619590 0.8257233 0.5211301
## 9 All.X.glm 0.7505695 0.8306911 0.5018214
## 1 MFO.myMFO_classfr 0.5375854 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5375854 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.4624146 0.4820959 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 10 944.5838 0.6
## 8 921.5765 0.6
## 4 NA 0.4
## 5 NA 0.5
## 11 NA 0.5
## 12 NA 0.4
## 7 928.1232 0.4
## 6 926.0247 0.4
## 9 937.8927 0.4
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_vars)) {
stop("not implemented yet")
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_vars)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "All.X.bayesglm OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 399 73
## Y 126 280dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## N.x All.X.bayesglm.importance N.y
## startprice 100.000000 100.000000 100.000000
## biddable 94.310602 94.310602 95.299950
## D.ratio.nstopwrds.nwrds 31.889829 31.889829 27.859787
## storage.fctr 29.860074 29.860074 26.541908
## .rnorm 23.254245 23.254245 22.114723
## D.npnct03.log 20.419225 20.419225 19.665570
## D.npnct15.log 20.275980 20.275980 20.305331
## D.T.use 19.691942 19.691942 18.295740
## D.T.excellent 19.434213 19.434213 19.194772
## D.npnct01.log 19.087993 19.087993 19.391086
## D.T.great 18.758918 18.758918 18.804571
## D.npnct10.log 18.523312 18.523312 18.664578
## D.npnct28.log 18.231293 18.231293 18.201710
## D.T.screen 18.072562 18.072562 19.726099
## D.T.veri 18.035507 18.035507 19.326997
## D.npnct09.log 17.729108 17.729108 17.784158
## D.T.work 17.468060 17.468060 17.034992
## D.T.used 17.464742 17.464742 19.516675
## D.T.good 17.160002 17.160002 17.750657
## D.T.new 16.952602 16.952602 15.800947
## D.T.box 16.334827 16.334827 16.090483
## D.T.scratch 16.074885 16.074885 18.006367
## D.T.like 15.956529 15.956529 16.404780
## D.ratio.sum.TfIdf.nwrds 15.904541 15.904541 16.430027
## D.npnct12.log 15.809966 15.809966 16.233551
## D.npnct08.log 15.782866 15.782866 16.632329
## D.T.ipad 15.017975 15.017975 16.197784
## D.T.this 14.985344 14.985344 15.843350
## D.npnct16.log 14.487031 14.487031 16.049213
## D.npnct06.log 14.453294 14.453294 15.820207
## D.T.condition 14.428406 14.428406 15.689924
## D.npnct14.log 13.696145 13.696145 15.315098
## D.sum.TfIdf 13.551795 13.551795 15.392458
## D.npnct11.log 13.283004 13.283004 17.371947
## color.fctr 13.264753 13.264753 13.442587
## D.npnct05.log 12.831702 12.831702 13.978284
## D.nstopwrds.log 12.426857 12.426857 15.264280
## D.ndgts.log 11.707870 11.707870 14.653974
## D.npnct13.log 11.662519 11.662519 13.686969
## D.npnct24.log 10.634368 10.634368 12.690023
## D.T.condit 9.083015 9.083015 11.418916
## D.nwrds.log 8.032188 8.032188 11.099441
## D.nuppr.log 6.029534 6.029534 9.062337
## D.nwrds.unq.log 5.328798 5.328798 9.229843
## D.nchrs.log 5.141309 5.141309 9.416932
## carrier.fctr 4.556164 4.556164 9.251206
## cellular.fctr 4.099331 4.099331 8.141456
## productline.fctr 1.204026 1.204026 4.897012
## condition.fctr 0.000000 0.000000 0.000000
## importance Final.bayesglm.importance
## startprice 100.000000 100.000000
## biddable 95.299950 95.299950
## D.ratio.nstopwrds.nwrds 27.859787 27.859787
## storage.fctr 26.541908 26.541908
## .rnorm 22.114723 22.114723
## D.npnct03.log 19.665570 19.665570
## D.npnct15.log 20.305331 20.305331
## D.T.use 18.295740 18.295740
## D.T.excellent 19.194772 19.194772
## D.npnct01.log 19.391086 19.391086
## D.T.great 18.804571 18.804571
## D.npnct10.log 18.664578 18.664578
## D.npnct28.log 18.201710 18.201710
## D.T.screen 19.726099 19.726099
## D.T.veri 19.326997 19.326997
## D.npnct09.log 17.784158 17.784158
## D.T.work 17.034992 17.034992
## D.T.used 19.516675 19.516675
## D.T.good 17.750657 17.750657
## D.T.new 15.800947 15.800947
## D.T.box 16.090483 16.090483
## D.T.scratch 18.006367 18.006367
## D.T.like 16.404780 16.404780
## D.ratio.sum.TfIdf.nwrds 16.430027 16.430027
## D.npnct12.log 16.233551 16.233551
## D.npnct08.log 16.632329 16.632329
## D.T.ipad 16.197784 16.197784
## D.T.this 15.843350 15.843350
## D.npnct16.log 16.049213 16.049213
## D.npnct06.log 15.820207 15.820207
## D.T.condition 15.689924 15.689924
## D.npnct14.log 15.315098 15.315098
## D.sum.TfIdf 15.392458 15.392458
## D.npnct11.log 17.371947 17.371947
## color.fctr 13.442587 13.442587
## D.npnct05.log 13.978284 13.978284
## D.nstopwrds.log 15.264280 15.264280
## D.ndgts.log 14.653974 14.653974
## D.npnct13.log 13.686969 13.686969
## D.npnct24.log 12.690023 12.690023
## D.T.condit 11.418916 11.418916
## D.nwrds.log 11.099441 11.099441
## D.nuppr.log 9.062337 9.062337
## D.nwrds.unq.log 9.229843 9.229843
## D.nchrs.log 9.416932 9.416932
## carrier.fctr 9.251206 9.251206
## cellular.fctr 8.141456 8.141456
## productline.fctr 4.897012 4.897012
## condition.fctr 0.000000 0.000000print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))##
## N Y
## 474 324# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)## Warning in rm(submit_df, tmp_OOBobs_df): object 'tmp_OOBobs_df' not found# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 131.645 137.455 5.81
## 17 display.session.info 10 0 137.455 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 60.744 99.432 38.688
## 10 fit.models 7 0 35.675 60.743 25.069
## 5 extract.features 3 0 15.058 32.681 17.624
## 12 fit.models 7 2 99.433 114.515 15.082
## 14 fit.data.training 8 0 118.984 126.206 7.222
## 2 inspect.data 2 0 8.131 14.007 5.876
## 16 predict.data.new 9 0 131.645 137.455 5.810
## 15 fit.data.training 8 1 126.206 131.644 5.439
## 13 fit.models 7 3 114.515 118.983 4.468
## 8 select.features 5 0 33.746 35.189 1.443
## 3 scrub.data 2 1 14.008 14.994 0.986
## 6 cluster.data 4 0 32.682 33.602 0.920
## 9 partition.data.training 6 0 35.189 35.675 0.486
## 1 import.data 1 0 7.793 8.130 0.337
## 7 manage.missing.data 4 1 33.602 33.745 0.143
## 4 transform.data 2 2 14.994 15.057 0.063
## duration
## 11 38.688
## 10 25.068
## 5 17.623
## 12 15.082
## 14 7.222
## 2 5.876
## 16 5.810
## 15 5.438
## 13 4.468
## 8 1.443
## 3 0.986
## 6 0.920
## 9 0.486
## 1 0.337
## 7 0.143
## 4 0.063
## [1] "Total Elapsed Time: 137.455 secs"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] gdata_2.17.0 randomForest_4.6-10 arm_1.8-6
## [4] lme4_1.1-8 Matrix_1.2-2 MASS_7.3-42
## [7] rpart.plot_1.5.2 rpart_4.1-10 ROCR_1.0-7
## [10] gplots_2.17.0 caTools_1.17.1 tm_0.6-2
## [13] NLP_0.1-8 stringr_1.0.0 dplyr_0.4.2
## [16] plyr_1.8.3 sqldf_0.4-10 RSQLite_1.0.0
## [19] DBI_0.3.1 gsubfn_0.6-6 proto_0.3-10
## [22] reshape2_1.4.1 doMC_1.3.3 iterators_1.0.7
## [25] foreach_1.4.2 doBy_4.5-13 survival_2.38-3
## [28] caret_6.0-47 ggplot2_1.0.1 lattice_0.20-33
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.11.6 class_7.3-13 gtools_3.5.0
## [4] assertthat_0.1 digest_0.6.8 slam_0.1-32
## [7] R6_2.1.0 BradleyTerry2_1.0-6 chron_2.3-47
## [10] coda_0.17-1 evaluate_0.7 e1071_1.6-4
## [13] lazyeval_0.1.10 minqa_1.2.4 SparseM_1.6
## [16] car_2.0-25 nloptr_1.0.4 rmarkdown_0.7
## [19] labeling_0.3 splines_3.2.1 munsell_0.4.2
## [22] compiler_3.2.1 mgcv_1.8-6 htmltools_0.2.6
## [25] nnet_7.3-10 codetools_0.2-14 brglm_0.5-9
## [28] bitops_1.0-6 nlme_3.1-121 gtable_0.1.2
## [31] magrittr_1.5 formatR_1.2 pROC_1.8
## [34] scales_0.2.5 KernSmooth_2.23-15 stringi_0.5-5
## [37] RColorBrewer_1.1-2 tools_3.2.1 abind_1.4-3
## [40] pbkrtest_0.4-2 yaml_2.1.13 colorspace_1.2-6
## [43] knitr_1.10.5 quantreg_5.11